diff --git a/examples/ODEs/Part_6_NetworkODE.ipynb b/examples/ODEs/Part_6_NetworkODE.ipynb
index 873ad5b7..be89b3bd 100644
--- a/examples/ODEs/Part_6_NetworkODE.ipynb
+++ b/examples/ODEs/Part_6_NetworkODE.ipynb
@@ -55,7 +55,7 @@
     {
      "data": {
       "text/plain": [
-       "<torch._C.Generator at 0x21d401c6830>"
+       "<torch._C.Generator at 0x10ae4aab0>"
       ]
      },
      "execution_count": 1,
@@ -77,7 +77,7 @@
     "\n",
     "# Neuromancer imports\n",
     "from neuromancer.psl.coupled_systems import *\n",
-    "from neuromancer.dynamics import integrators, ode, physics, interpolation\n",
+    "from neuromancer.dynamics import integrators, ode, physics\n",
     "from neuromancer.dataset import DictDataset\n",
     "from neuromancer.constraint import variable\n",
     "from neuromancer.problem import Problem\n",
@@ -135,11 +135,11 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21d6c357730>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c357790>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c3577c0>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c3578b0>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c3579a0>]"
+       "[<matplotlib.lines.Line2D at 0x12ed4e260>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed4e200>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed68d60>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed68d30>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed68d00>]"
       ]
      },
      "execution_count": 3,
@@ -148,7 +148,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -176,12 +176,12 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21d6c452b90>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c452bf0>,\n",
-       " <matplotlib.lines.Line2D at 0x21d1ff230a0>,\n",
-       " <matplotlib.lines.Line2D at 0x21d1ff229b0>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c2e0b20>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c452e30>]"
+       "[<matplotlib.lines.Line2D at 0x12ed66530>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed664d0>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed662f0>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed66200>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed66110>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed66020>]"
       ]
      },
      "execution_count": 4,
@@ -190,7 +190,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -255,7 +255,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zones = [physics.RCNode(C=nn.Parameter(torch.tensor(5.0)),scaling=1.0e-5) for i in range(5)]  # heterogeneous population w/ identical physics"
+    "# Define the states\n",
+    "states = {}\n",
+    "states['T_1'] = 0\n",
+    "states['T_2'] = 1\n",
+    "states['T_3'] = 2\n",
+    "states['T_4'] = 3\n",
+    "states['T_5'] = 4\n",
+    "states['T_6'] = 5\n",
+    "states['T_7'] = 6\n",
+    "states['T_8'] = 7\n",
+    "states['T_9'] = 8\n",
+    "states['T_10'] = 9\n",
+    "states['T_11'] = 10\n",
+    "\n",
+    "# Model construction\n",
+    "keys = list(states.keys())\n",
+    "zones = [physics.RCNode(in_keys=[keys[i]], state_keys=[keys[i]], \n",
+    "                        C=nn.Parameter(torch.tensor(5.0)),scaling=1.0e-5) for i in range(5)]"
    ]
   },
   {
@@ -271,7 +288,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "heaters = [physics.SourceSink() for i in range(5)] # define heaters"
+    "heaters = [physics.SourceSink(state_keys=[keys[i+len(zones)]], in_keys=[keys[i+len(zones)]]) for i in range(5)] \n"
    ]
   },
   {
@@ -287,38 +304,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "outside = [physics.SourceSink()]  \n",
+    "outside = [physics.SourceSink(state_keys=[keys[-1]], in_keys=[keys[-1]])]\n",
     "\n",
     "# join lists:\n",
     "agents = zones + heaters + outside"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we connect our agents together in a graph, we need to define a mapping between our agents in the list and the indices of their respective states in the dataset. For this, we use a quick helper function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[OrderedDict([('T', 0)]), OrderedDict([('T', 1)]), OrderedDict([('T', 2)]), OrderedDict([('T', 3)]), OrderedDict([('T', 4)]), OrderedDict([('T', 5)]), OrderedDict([('T', 6)]), OrderedDict([('T', 7)]), OrderedDict([('T', 8)]), OrderedDict([('T', 9)]), OrderedDict([('T', 10)])]\n"
-     ]
-    }
-   ],
-   "source": [
-    "map = physics.map_from_agents(agents)\n",
-    "# Let's take a look at this 'map':\n",
-    "print(map)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -330,14 +321,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Assuming new <class 'neuromancer.dynamics.physics.DeltaTemp'> for each element in edge list.\n",
       "14\n",
       "DeltaTemp()\n",
       "[array([0, 1])]\n"
@@ -346,28 +336,18 @@
    ],
    "source": [
     "# Helper function for constructing couplings based on desired edge physics and an edge list:\n",
-    "def generate_parameterized_edges(physics,edge_list):\n",
+    "def generate_deltaTemp_edges(physics,edge_list,agents):\n",
     "    \"\"\"\n",
     "    Quick helper function to construct edge physics/objects from adj. list:\n",
     "    \"\"\"\n",
-    "\n",
     "    couplings = []\n",
-    "    if isinstance(physics,nn.Module): # is \"physics\" an instance or a class?\n",
-    "        # If we're in here, we expect one instance of \"physics\" for all edges in edge_list (homogeneous edges)\n",
-    "        physics.pins = edge_list\n",
-    "        couplings.append(physics)\n",
-    "        print(f'Broadcasting {physics} to all elements in edge list.')\n",
-    "    else: \n",
-    "        # If we're in here, we expect different \"physics\" for each edge in edge_list (heterogeneous edges)\n",
-    "        for edge in edge_list:\n",
-    "            agent = physics(R=nn.Parameter(torch.tensor(50.0)),pins=[edge])\n",
-    "            couplings.append(agent)\n",
-    "\n",
-    "        print(f'Assuming new {physics} for each element in edge list.')\n",
+    "    for edge in edge_list:\n",
+    "        agent = physics(in_keys=[*agents[edge[1]].in_keys,*agents[edge[0]].in_keys],R=nn.Parameter(torch.tensor(50.0)),pins=[edge])\n",
+    "        couplings.append(agent)\n",
     "\n",
     "    return couplings\n",
     "\n",
-    "couplings = generate_parameterized_edges(physics.DeltaTemp,list(adj.T))    # Heterogeneous edges of same physics\n",
+    "couplings = generate_deltaTemp_edges(physics.DeltaTemp,list(adj.T),agents)    # Heterogeneous edges of same physics\n",
     "\n",
     "# What do we have so far?\n",
     "print(len(couplings))\n",
@@ -386,23 +366,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Couple w/ outside temp:\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,5]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,5]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,5]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,5]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,5]]))\n",
+    "outside_list = [[0,5],[1,5],[2,5],[3,5],[4,5]]\n",
+    "out_couplings = generate_deltaTemp_edges(physics.DeltaTemp,outside_list,agents)\n",
     "\n",
     "# Couple w/ individual sources:\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,6]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,7]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,8]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,9]]))\n",
-    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,10]]))"
+    "source_list = [[0,6],[1,7],[2,8],[3,9],[4,10]]\n",
+    "source_couplings = generate_deltaTemp_edges(physics.DeltaTemp,source_list,agents)"
    ]
   },
   {
@@ -414,19 +388,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "model_ode = ode.GeneralNetworkedODE(\n",
-    "    map = map,\n",
+    "    states = states,\n",
     "    agents = agents,\n",
     "    couplings = couplings,\n",
     "    insize = s.nx+s.nu,\n",
-    "    outsize = s.nx,\n",
-    "    inductive_bias=\"compositional\")\n",
+    "    outsize = s.nx)\n",
     "\n",
-    "fx_int = integrators.RK2(model_ode, h=1.0)\n",
+    "fx_int = integrators.Euler(model_ode, h=1.0)\n",
     "\n",
     "dynamics_model = System([Node(fx_int,['xn','U'],['xn'])])"
    ]
@@ -445,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +426,7 @@
      "output_type": "stream",
      "text": [
       "None\n",
-      "Number of parameters: 29\n"
+      "Number of parameters: 19\n"
      ]
     }
    ],
@@ -503,1061 +476,884 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 0\ttrain_loss: 0.01284\tdev_loss: 0.05561\teltime:  0.25205\n",
-      "epoch: 1\ttrain_loss: 0.01278\tdev_loss: 0.05536\teltime:  0.57704\n",
-      "epoch: 2\ttrain_loss: 0.01272\tdev_loss: 0.05510\teltime:  0.84812\n",
-      "epoch: 3\ttrain_loss: 0.01266\tdev_loss: 0.05485\teltime:  1.12015\n",
-      "epoch: 4\ttrain_loss: 0.01260\tdev_loss: 0.05460\teltime:  1.37500\n",
-      "epoch: 5\ttrain_loss: 0.01254\tdev_loss: 0.05435\teltime:  1.67538\n",
-      "epoch: 6\ttrain_loss: 0.01248\tdev_loss: 0.05410\teltime:  1.93220\n",
-      "epoch: 7\ttrain_loss: 0.01242\tdev_loss: 0.05385\teltime:  2.20841\n",
-      "epoch: 8\ttrain_loss: 0.01236\tdev_loss: 0.05360\teltime:  2.49175\n",
-      "epoch: 9\ttrain_loss: 0.01231\tdev_loss: 0.05335\teltime:  2.87653\n",
-      "epoch: 10\ttrain_loss: 0.01225\tdev_loss: 0.05310\teltime:  3.13448\n",
-      "epoch: 11\ttrain_loss: 0.01219\tdev_loss: 0.05285\teltime:  3.37705\n",
-      "epoch: 12\ttrain_loss: 0.01213\tdev_loss: 0.05260\teltime:  3.66636\n",
-      "epoch: 13\ttrain_loss: 0.01207\tdev_loss: 0.05236\teltime:  3.92337\n",
-      "epoch: 14\ttrain_loss: 0.01201\tdev_loss: 0.05211\teltime:  4.16764\n",
-      "epoch: 15\ttrain_loss: 0.01196\tdev_loss: 0.05187\teltime:  4.45761\n",
-      "epoch: 16\ttrain_loss: 0.01190\tdev_loss: 0.05163\teltime:  4.73752\n",
-      "epoch: 17\ttrain_loss: 0.01184\tdev_loss: 0.05138\teltime:  5.01089\n",
-      "epoch: 18\ttrain_loss: 0.01178\tdev_loss: 0.05114\teltime:  5.28018\n",
-      "epoch: 19\ttrain_loss: 0.01173\tdev_loss: 0.05090\teltime:  5.53978\n",
-      "epoch: 20\ttrain_loss: 0.01167\tdev_loss: 0.05066\teltime:  5.81230\n",
-      "epoch: 21\ttrain_loss: 0.01161\tdev_loss: 0.05042\teltime:  6.09818\n",
-      "epoch: 22\ttrain_loss: 0.01156\tdev_loss: 0.05018\teltime:  6.35454\n",
-      "epoch: 23\ttrain_loss: 0.01150\tdev_loss: 0.04994\teltime:  6.62604\n",
-      "epoch: 24\ttrain_loss: 0.01145\tdev_loss: 0.04970\teltime:  6.88568\n",
-      "epoch: 25\ttrain_loss: 0.01139\tdev_loss: 0.04947\teltime:  7.14948\n",
-      "epoch: 26\ttrain_loss: 0.01133\tdev_loss: 0.04923\teltime:  7.42847\n",
-      "epoch: 27\ttrain_loss: 0.01128\tdev_loss: 0.04900\teltime:  7.73237\n",
-      "epoch: 28\ttrain_loss: 0.01122\tdev_loss: 0.04876\teltime:  8.00197\n",
-      "epoch: 29\ttrain_loss: 0.01117\tdev_loss: 0.04853\teltime:  8.24394\n",
-      "epoch: 30\ttrain_loss: 0.01112\tdev_loss: 0.04830\teltime:  8.50300\n",
-      "epoch: 31\ttrain_loss: 0.01106\tdev_loss: 0.04806\teltime:  8.76868\n",
-      "epoch: 32\ttrain_loss: 0.01101\tdev_loss: 0.04783\teltime:  9.04321\n",
-      "epoch: 33\ttrain_loss: 0.01095\tdev_loss: 0.04760\teltime:  9.35119\n",
-      "epoch: 34\ttrain_loss: 0.01090\tdev_loss: 0.04737\teltime:  9.62027\n",
-      "epoch: 35\ttrain_loss: 0.01085\tdev_loss: 0.04715\teltime:  9.88775\n",
-      "epoch: 36\ttrain_loss: 0.01079\tdev_loss: 0.04692\teltime:  10.14805\n",
-      "epoch: 37\ttrain_loss: 0.01074\tdev_loss: 0.04669\teltime:  10.43815\n",
-      "epoch: 38\ttrain_loss: 0.01069\tdev_loss: 0.04647\teltime:  10.69076\n",
-      "epoch: 39\ttrain_loss: 0.01063\tdev_loss: 0.04624\teltime:  10.93361\n",
-      "epoch: 40\ttrain_loss: 0.01058\tdev_loss: 0.04602\teltime:  11.16591\n",
-      "epoch: 41\ttrain_loss: 0.01053\tdev_loss: 0.04579\teltime:  11.42002\n",
-      "epoch: 42\ttrain_loss: 0.01048\tdev_loss: 0.04557\teltime:  11.69303\n",
-      "epoch: 43\ttrain_loss: 0.01042\tdev_loss: 0.04535\teltime:  11.93455\n",
-      "epoch: 44\ttrain_loss: 0.01037\tdev_loss: 0.04513\teltime:  12.24176\n",
-      "epoch: 45\ttrain_loss: 0.01032\tdev_loss: 0.04491\teltime:  12.51075\n",
-      "epoch: 46\ttrain_loss: 0.01027\tdev_loss: 0.04469\teltime:  12.82642\n",
-      "epoch: 47\ttrain_loss: 0.01022\tdev_loss: 0.04447\teltime:  13.08317\n",
-      "epoch: 48\ttrain_loss: 0.01017\tdev_loss: 0.04425\teltime:  13.34523\n",
-      "epoch: 49\ttrain_loss: 0.01012\tdev_loss: 0.04404\teltime:  13.59660\n",
-      "epoch: 50\ttrain_loss: 0.01007\tdev_loss: 0.04382\teltime:  13.83879\n",
-      "epoch: 51\ttrain_loss: 0.01002\tdev_loss: 0.04361\teltime:  14.11305\n",
-      "epoch: 52\ttrain_loss: 0.00997\tdev_loss: 0.04339\teltime:  14.36309\n",
-      "epoch: 53\ttrain_loss: 0.00992\tdev_loss: 0.04318\teltime:  14.61886\n",
-      "epoch: 54\ttrain_loss: 0.00987\tdev_loss: 0.04297\teltime:  14.89964\n",
-      "epoch: 55\ttrain_loss: 0.00982\tdev_loss: 0.04275\teltime:  15.18928\n",
-      "epoch: 56\ttrain_loss: 0.00977\tdev_loss: 0.04254\teltime:  15.44572\n",
-      "epoch: 57\ttrain_loss: 0.00972\tdev_loss: 0.04233\teltime:  15.74823\n",
-      "epoch: 58\ttrain_loss: 0.00967\tdev_loss: 0.04213\teltime:  15.98673\n",
-      "epoch: 59\ttrain_loss: 0.00962\tdev_loss: 0.04192\teltime:  16.24532\n",
-      "epoch: 60\ttrain_loss: 0.00957\tdev_loss: 0.04171\teltime:  16.51833\n",
-      "epoch: 61\ttrain_loss: 0.00953\tdev_loss: 0.04150\teltime:  16.80284\n",
-      "epoch: 62\ttrain_loss: 0.00948\tdev_loss: 0.04130\teltime:  17.07744\n",
-      "epoch: 63\ttrain_loss: 0.00943\tdev_loss: 0.04109\teltime:  17.36433\n",
-      "epoch: 64\ttrain_loss: 0.00938\tdev_loss: 0.04089\teltime:  17.63546\n",
-      "epoch: 65\ttrain_loss: 0.00933\tdev_loss: 0.04068\teltime:  17.92005\n",
-      "epoch: 66\ttrain_loss: 0.00929\tdev_loss: 0.04048\teltime:  18.22104\n",
-      "epoch: 67\ttrain_loss: 0.00924\tdev_loss: 0.04028\teltime:  18.52142\n",
-      "epoch: 68\ttrain_loss: 0.00919\tdev_loss: 0.04008\teltime:  18.78927\n",
-      "epoch: 69\ttrain_loss: 0.00915\tdev_loss: 0.03988\teltime:  19.10708\n",
-      "epoch: 70\ttrain_loss: 0.00910\tdev_loss: 0.03968\teltime:  19.37584\n",
-      "epoch: 71\ttrain_loss: 0.00905\tdev_loss: 0.03948\teltime:  19.66826\n",
-      "epoch: 72\ttrain_loss: 0.00901\tdev_loss: 0.03928\teltime:  19.95548\n",
-      "epoch: 73\ttrain_loss: 0.00896\tdev_loss: 0.03909\teltime:  20.20967\n",
-      "epoch: 74\ttrain_loss: 0.00892\tdev_loss: 0.03889\teltime:  20.49080\n",
-      "epoch: 75\ttrain_loss: 0.00887\tdev_loss: 0.03869\teltime:  20.78600\n",
-      "epoch: 76\ttrain_loss: 0.00883\tdev_loss: 0.03850\teltime:  21.04587\n",
-      "epoch: 77\ttrain_loss: 0.00878\tdev_loss: 0.03830\teltime:  21.35855\n",
-      "epoch: 78\ttrain_loss: 0.00874\tdev_loss: 0.03811\teltime:  21.64326\n",
-      "epoch: 79\ttrain_loss: 0.00869\tdev_loss: 0.03792\teltime:  21.89392\n",
-      "epoch: 80\ttrain_loss: 0.00865\tdev_loss: 0.03773\teltime:  22.15631\n",
-      "epoch: 81\ttrain_loss: 0.00860\tdev_loss: 0.03754\teltime:  22.44438\n",
-      "epoch: 82\ttrain_loss: 0.00856\tdev_loss: 0.03735\teltime:  22.67984\n",
-      "epoch: 83\ttrain_loss: 0.00851\tdev_loss: 0.03716\teltime:  22.98433\n",
-      "epoch: 84\ttrain_loss: 0.00847\tdev_loss: 0.03697\teltime:  23.25885\n",
-      "epoch: 85\ttrain_loss: 0.00843\tdev_loss: 0.03678\teltime:  23.51535\n",
-      "epoch: 86\ttrain_loss: 0.00838\tdev_loss: 0.03660\teltime:  23.83182\n",
-      "epoch: 87\ttrain_loss: 0.00834\tdev_loss: 0.03641\teltime:  24.11719\n",
-      "epoch: 88\ttrain_loss: 0.00830\tdev_loss: 0.03622\teltime:  24.35816\n",
-      "epoch: 89\ttrain_loss: 0.00825\tdev_loss: 0.03604\teltime:  24.66478\n",
-      "epoch: 90\ttrain_loss: 0.00821\tdev_loss: 0.03586\teltime:  24.91851\n",
-      "epoch: 91\ttrain_loss: 0.00817\tdev_loss: 0.03567\teltime:  25.17670\n",
-      "epoch: 92\ttrain_loss: 0.00813\tdev_loss: 0.03549\teltime:  25.48013\n",
-      "epoch: 93\ttrain_loss: 0.00809\tdev_loss: 0.03531\teltime:  25.75203\n",
-      "epoch: 94\ttrain_loss: 0.00804\tdev_loss: 0.03513\teltime:  26.02120\n",
-      "epoch: 95\ttrain_loss: 0.00800\tdev_loss: 0.03495\teltime:  26.26277\n",
-      "epoch: 96\ttrain_loss: 0.00796\tdev_loss: 0.03477\teltime:  26.53731\n",
-      "epoch: 97\ttrain_loss: 0.00792\tdev_loss: 0.03459\teltime:  26.80352\n",
-      "epoch: 98\ttrain_loss: 0.00788\tdev_loss: 0.03441\teltime:  27.07217\n",
-      "epoch: 99\ttrain_loss: 0.00784\tdev_loss: 0.03424\teltime:  27.33856\n",
-      "epoch: 100\ttrain_loss: 0.00780\tdev_loss: 0.03406\teltime:  27.60444\n",
-      "epoch: 101\ttrain_loss: 0.00775\tdev_loss: 0.03388\teltime:  27.86886\n",
-      "epoch: 102\ttrain_loss: 0.00771\tdev_loss: 0.03371\teltime:  28.14076\n",
-      "epoch: 103\ttrain_loss: 0.00767\tdev_loss: 0.03353\teltime:  28.44152\n",
-      "epoch: 104\ttrain_loss: 0.00763\tdev_loss: 0.03336\teltime:  28.72652\n",
-      "epoch: 105\ttrain_loss: 0.00759\tdev_loss: 0.03319\teltime:  28.95264\n",
-      "epoch: 106\ttrain_loss: 0.00756\tdev_loss: 0.03302\teltime:  29.22797\n",
-      "epoch: 107\ttrain_loss: 0.00752\tdev_loss: 0.03284\teltime:  29.51104\n",
-      "epoch: 108\ttrain_loss: 0.00748\tdev_loss: 0.03267\teltime:  29.75755\n",
-      "epoch: 109\ttrain_loss: 0.00744\tdev_loss: 0.03250\teltime:  30.00679\n",
-      "epoch: 110\ttrain_loss: 0.00740\tdev_loss: 0.03234\teltime:  30.28352\n",
-      "epoch: 111\ttrain_loss: 0.00736\tdev_loss: 0.03217\teltime:  30.59267\n",
-      "epoch: 112\ttrain_loss: 0.00732\tdev_loss: 0.03200\teltime:  30.85618\n",
-      "epoch: 113\ttrain_loss: 0.00728\tdev_loss: 0.03183\teltime:  31.11472\n",
-      "epoch: 114\ttrain_loss: 0.00724\tdev_loss: 0.03167\teltime:  31.37679\n",
-      "epoch: 115\ttrain_loss: 0.00720\tdev_loss: 0.03150\teltime:  31.62363\n",
-      "epoch: 116\ttrain_loss: 0.00717\tdev_loss: 0.03133\teltime:  31.89021\n",
-      "epoch: 117\ttrain_loss: 0.00713\tdev_loss: 0.03117\teltime:  32.16729\n",
-      "epoch: 118\ttrain_loss: 0.00709\tdev_loss: 0.03101\teltime:  32.46698\n",
-      "epoch: 119\ttrain_loss: 0.00705\tdev_loss: 0.03085\teltime:  32.71818\n",
-      "epoch: 120\ttrain_loss: 0.00702\tdev_loss: 0.03068\teltime:  32.99366\n",
-      "epoch: 121\ttrain_loss: 0.00698\tdev_loss: 0.03052\teltime:  33.25502\n",
-      "epoch: 122\ttrain_loss: 0.00694\tdev_loss: 0.03036\teltime:  33.53812\n",
-      "epoch: 123\ttrain_loss: 0.00691\tdev_loss: 0.03020\teltime:  33.81944\n",
-      "epoch: 124\ttrain_loss: 0.00687\tdev_loss: 0.03004\teltime:  34.11512\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 125\ttrain_loss: 0.00683\tdev_loss: 0.02988\teltime:  34.37020\n",
-      "epoch: 126\ttrain_loss: 0.00680\tdev_loss: 0.02972\teltime:  34.67126\n",
-      "epoch: 127\ttrain_loss: 0.00676\tdev_loss: 0.02957\teltime:  34.97128\n",
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-     "text": [
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-     ]
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-    {
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-     "output_type": "stream",
-     "text": [
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+      "epoch: 837\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.29851\n",
+      "epoch: 838\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.38835\n",
+      "epoch: 839\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.47646\n",
+      "epoch: 840\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.56769\n",
+      "epoch: 841\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.65771\n",
+      "epoch: 842\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.74871\n",
+      "epoch: 843\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.84223\n",
+      "epoch: 844\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  77.98524\n",
+      "epoch: 845\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.08419\n",
+      "epoch: 846\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.17234\n",
+      "epoch: 847\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.25872\n",
+      "epoch: 848\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.34576\n",
+      "epoch: 849\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.43512\n",
+      "epoch: 850\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.52258\n",
+      "epoch: 851\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.60983\n",
+      "epoch: 852\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.69777\n",
+      "epoch: 853\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.78408\n",
+      "epoch: 854\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.87119\n",
+      "epoch: 855\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  78.95963\n",
+      "epoch: 856\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.04573\n",
+      "epoch: 857\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.13295\n",
+      "epoch: 858\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.22003\n",
+      "epoch: 859\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.30772\n",
+      "epoch: 860\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.39578\n",
+      "epoch: 861\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.48253\n",
+      "epoch: 862\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.56786\n",
+      "epoch: 863\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.65590\n",
+      "epoch: 864\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.74112\n",
+      "epoch: 865\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.82837\n",
+      "epoch: 866\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  79.91862\n",
+      "epoch: 867\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  80.00419\n",
+      "epoch: 868\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  80.08961\n",
+      "epoch: 869\ttrain_loss: 0.00021\tdev_loss: 0.00049\teltime:  80.17640\n",
+      "Early stopping!!!\n"
      ]
     }
    ],
@@ -1576,26 +1372,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x21d6c4f37f0>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c4f3820>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c4f3850>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c4f0760>,\n",
-       " <matplotlib.lines.Line2D at 0x21d6c4f1960>]"
+       "[<matplotlib.lines.Line2D at 0x12ed5c490>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed5c520>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed5c340>,\n",
+       " <matplotlib.lines.Line2D at 0x12ed5c400>,\n",
+       " <matplotlib.lines.Line2D at 0x12eff4400>]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1622,9 +1418,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neuromancer",
+   "display_name": "nm14",
    "language": "python",
-   "name": "neuromancer"
+   "name": "nm14"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/examples/ODEs/Part_6_NetworkODE.py b/examples/ODEs/Part_6_NetworkODE.py
index ebd4eaf7..d1977cf2 100644
--- a/examples/ODEs/Part_6_NetworkODE.py
+++ b/examples/ODEs/Part_6_NetworkODE.py
@@ -1,4 +1,4 @@
-# Numpy + plotting utilities + ordered dicts
+# %% Numpy + plotting utilities + ordered dicts
 import numpy as np
 import matplotlib.pyplot as plt
 from collections import OrderedDict
@@ -11,7 +11,7 @@
 
 # Neuromancer imports
 from neuromancer.psl.coupled_systems import *
-from neuromancer.dynamics import integrators, ode, physics, interpolation
+from neuromancer.dynamics import integrators, ode, physics
 from neuromancer.dataset import DictDataset
 from neuromancer.constraint import variable
 from neuromancer.problem import Problem
@@ -24,7 +24,7 @@
 np.random.seed(200)
 torch.manual_seed(0)
 
-
+# Define Network and datasets
 adj = np.array([[0,1],[0,2],[0,3],[1,0],[1,3],[1,4],[2,0],[2,3],[3,0],[3,1],[3,2],[3,4],[4,1],[4,3]]).T
 s = RC_Network(nx=5, adj=adj)
 nsim = 500
@@ -48,42 +48,46 @@
 train_dataset, dev_dataset, = [DictDataset(d, name=n) for d, n in zip([train_data, dev_data], ['train', 'dev'])]
 train_loader, dev_loader, test_loader = [DataLoader(d, batch_size=nsim//nstep, collate_fn=d.collate_fn, shuffle=True) for d in [train_dataset, dev_dataset, dev_dataset]]
 
-zones = [physics.RCNode(C=nn.Parameter(torch.tensor(5.0)),scaling=1.0e-5) for i in range(5)]  # heterogeneous population w/ identical physics
 
-heaters = [physics.SourceSink() for i in range(5)] # define heaters
+# Define the states
+states = {}
+states['T_1'] = 0
+states['T_2'] = 1
+states['T_3'] = 2
+states['T_4'] = 3
+states['T_5'] = 4
+states['T_6'] = 5
+states['T_7'] = 6
+states['T_8'] = 7
+states['T_9'] = 8
+states['T_10'] = 9
+states['T_11'] = 10
+
+# Model construction
+keys = list(states.keys())
+zones = [physics.RCNode(in_keys=[keys[i]], state_keys=[keys[i]], 
+                        C=nn.Parameter(torch.tensor(5.0)),scaling=1.0e-5) for i in range(5)]
 
-outside = [physics.SourceSink()]
+heaters = [physics.SourceSink(state_keys=[keys[i+len(zones)]], in_keys=[keys[i+len(zones)]]) for i in range(5)] # define heaters
+
+outside = [physics.SourceSink(state_keys=[keys[-1]], in_keys=[keys[-1]])]
 
 # join lists:
 agents = zones + heaters + outside
 
-map = physics.map_from_agents(agents)
-# Let's take a look at this 'map':
-print(map)
-
 # Helper function for constructing couplings based on desired edge physics and an edge list:
-def generate_parameterized_edges(physics,edge_list):
+def generate_deltaTemp_edges(physics,edge_list,agents):
     """
     Quick helper function to construct edge physics/objects from adj. list:
     """
-
     couplings = []
-    if isinstance(physics,nn.Module): # is "physics" an instance or a class?
-        # If we're in here, we expect one instance of "physics" for all edges in edge_list (homogeneous edges)
-        physics.pins = edge_list
-        couplings.append(physics)
-        print(f'Broadcasting {physics} to all elements in edge list.')
-    else:
-        # If we're in here, we expect different "physics" for each edge in edge_list (heterogeneous edges)
-        for edge in edge_list:
-            agent = physics(R=nn.Parameter(torch.tensor(50.0)),pins=[edge])
-            couplings.append(agent)
-
-        print(f'Assuming new {physics} for each element in edge list.')
+    for edge in edge_list:
+        agent = physics(in_keys=[*agents[edge[1]].in_keys,*agents[edge[0]].in_keys],R=nn.Parameter(torch.tensor(50.0)),pins=[edge])
+        couplings.append(agent)
 
     return couplings
 
-couplings = generate_parameterized_edges(physics.DeltaTemp,list(adj.T))    # Heterogeneous edges of same physics
+couplings = generate_deltaTemp_edges(physics.DeltaTemp,list(adj.T),agents)    # Heterogeneous edges of same physics
 
 # What do we have so far?
 print(len(couplings))
@@ -93,28 +97,25 @@ def generate_parameterized_edges(physics,edge_list):
 print(couplings[0].pins)
 
 # Couple w/ outside temp:
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,5]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,5]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,5]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,5]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,5]]))
+outside_list = [[0,5],[1,5],[2,5],[3,5],[4,5]]
+out_couplings = generate_deltaTemp_edges(physics.DeltaTemp,outside_list,agents)
 
 # Couple w/ individual sources:
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,6]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,7]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,8]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,9]]))
-couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,10]]))
+source_list = [[0,6],[1,7],[2,8],[3,9],[4,10]]
+source_couplings = generate_deltaTemp_edges(physics.DeltaTemp,source_list,agents)
+
+couplings += out_couplings + source_couplings
 
+# Model ODE RHS instantiation
 model_ode = ode.GeneralNetworkedODE(
-    map = map,
+    states=states,
     agents = agents,
     couplings = couplings,
     insize = s.nx+s.nu,
-    outsize = s.nx,
-    inductive_bias="compositional")
+    outsize = s.nx)
 
-fx_int = integrators.RK2(model_ode, h=1.0)
+# Integrator instantiation
+fx_int = integrators.RK4(model_ode, h=1.0)
 
 dynamics_model = System([Node(fx_int,['xn','U'],['xn'])])
 
@@ -167,4 +168,5 @@ def generate_parameterized_edges(physics,edge_list):
 
 plt.figure()
 plt.plot(sol.detach().numpy(),label='model', color = 'black')
-plt.plot(s_test['X'][:,:5],label = 'data', color = 'red')
\ No newline at end of file
+plt.plot(s_test['X'][:,:5],label = 'data', color = 'red')
+# %%
diff --git a/examples/ODEs/Part_7_DAE.ipynb b/examples/ODEs/Part_7_DAE.ipynb
new file mode 100755
index 00000000..0a05a131
--- /dev/null
+++ b/examples/ODEs/Part_7_DAE.ipynb
@@ -0,0 +1,11963 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data-driven modeling of a Tank/Manifold System\n",
+    "\n",
+    "This tutorial performs the data-driven modeling of a pair of tanks connected by a common manifold.\n",
+    "\n",
+    "**Problem Setup:**\n",
+    "\n",
+    "Consider two water tanks of different area-height profiles connected at their base by a common pipe/manifold network that is fed by a pump:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Tank-manifold diagram](figs/manifold.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, a volumetric flow out of the pump enters a manifold where the flow splits into two streams; one into each tank. The split of flow between these two streams is equal to the input. In mathematical terms:\n",
+    "$$\n",
+    "q_\\text{pump} = q_2 + q_2.\n",
+    "$$\n",
+    "Similarly, we can write down evolution equations for the heights of the tanks:\n",
+    "$$\n",
+    "\\frac{dh_1}{dt} = \\frac{1}{A_1 \\left( h_1 \\right)}q_1\n",
+    "$$\n",
+    "$$\n",
+    "\\frac{dh_2}{dt} = \\frac{1}{A_2 \\left( h_2 \\right)}q_2 . \n",
+    "$$\n",
+    "\n",
+    "Now, suppose we have two goals: from measurements of tank heights and flows and knowledge of $A_1(h)$, (i) predict the evolution of the system, and (ii) learn the other area-height tank profile. Let's start by training a Neural ODE to learn the dynamics of the system. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Modeling in Neuromancer:**\n",
+    "\n",
+    "Let's do this task in Neuromancer. Here are the relevant imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "import os\n",
+    "\n",
+    "import neuromancer.slim as slim\n",
+    "from neuromancer.modules import blocks, activations\n",
+    "from neuromancer.dynamics import integrators, ode, physics\n",
+    "from neuromancer.trainer import Trainer\n",
+    "from neuromancer.problem import Problem\n",
+    "from neuromancer.dataset import DictDataset\n",
+    "from neuromancer.loss import PenaltyLoss\n",
+    "from neuromancer.constraint import variable, Objective\n",
+    "from neuromancer.system import Node, System\n",
+    "from neuromancer.loggers import BasicLogger\n",
+    "\n",
+    "from collections import OrderedDict\n",
+    "from abc import ABC, abstractmethod\n",
+    "\n",
+    "torch.manual_seed(0)\n",
+    "device = 'cpu'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also want to keep our plots clean and uniform - let's set the defaults now:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams[\"font.family\"] = \"serif\"\n",
+    "#plt.rcParams[\"font.serif\"] = [\"Times\"]\n",
+    "plt.rcParams['figure.dpi'] = 300\n",
+    "plt.rcParams.update({'font.size': 10})\n",
+    "\n",
+    "params = {'legend.fontsize': 10,\n",
+    "         'axes.labelsize': 10,\n",
+    "         'axes.titlesize': 10,\n",
+    "         'xtick.labelsize': 10,\n",
+    "         'ytick.labelsize': 10}\n",
+    "plt.rcParams.update(params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Data Loading and preparation**\n",
+    "\n",
+    "For this problem, we load data from a text file and construct the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.float32(np.loadtxt('data/tanks.dat'))\n",
+    "data=data[1:497,]\n",
+    "area_data = np.loadtxt('data/area.dat')\n",
+    "time = np.float32(np.linspace(0.0,len(data[:,0])-1,len(data[:,0])).reshape(-1, 1))\n",
+    "U = time*0.0 + 0.5\n",
+    "\n",
+    "train_data = {'Y': data[1:], 'X': data[1:], 'Time': time[1:], 'U': U[1:] }\n",
+    "dev_data = train_data\n",
+    "test_data = train_data\n",
+    "\n",
+    "nsim = data.shape[0]\n",
+    "nx = data.shape[1]\n",
+    "nstep = 15\n",
+    "\n",
+    "for d in [train_data, dev_data]:\n",
+    "    d['X'] = d['X'].reshape(nsim//nstep, nstep, nx)\n",
+    "    d['Y'] = d['Y'].reshape(nsim//nstep, nstep, nx)\n",
+    "    d['xn'] = d['X'][:, 0:1, :] # Add an initial condition to start the system loop\n",
+    "    d['Time'] = d['Time'].reshape(nsim//nstep, nstep, 1)\n",
+    "    d['U'] = d['U'].reshape(nsim//nstep, nstep, 1)\n",
+    "\n",
+    "train_dataset, dev_dataset, = [DictDataset(d, name=n) for d, n in zip([train_data, dev_data], ['train', 'dev'])]\n",
+    "train_loader, dev_loader, test_loader = [DataLoader(d, batch_size=nsim//nstep, collate_fn=d.collate_fn, shuffle=True) for d in [train_dataset, dev_dataset, dev_dataset]]\n",
+    "\n",
+    "nx = 4 # set the state dimension\n",
+    "nu = 1 # set the exogenous input dimension\n",
+    "\n",
+    "# State names if we need them (we do)\n",
+    "states = {}\n",
+    "states['h_1'] = 0\n",
+    "states['h_2'] = 1\n",
+    "states['m_1'] = 2\n",
+    "states['m_2'] = 3\n",
+    "states['m'] = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Data Visualization:**\n",
+    "\n",
+    "Now that we have our data loaded, we can take a look at the system dynamics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time,data[:,0])\n",
+    "plt.xlim([0,500])\n",
+    "plt.ylim([0,40])\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Height\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CYII=",
+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(time,data[:,[2,3]])\n",
+    "plt.plot(time,data[:,2]+data[:,3])\n",
+    "plt.xlim([0,500])\n",
+    "plt.ylim([0,0.6])\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Volumetric Flow\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a couple of things to note here. First, the heights of the two tanks are equivalent for all time. This is because although the tanks are of different area-height profiles, and therefore volumes, their evolution is constrained such that each has the same hydrostatic pressure head. Similarly, the volumetric flow into each tank varies as a function of time. Notice how adding the curves together yield the third (constant) curve at 0.5. This is because of the conservation relationship at the manifold: what goes in must come out!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Model Construction:**\n",
+    "\n",
+    "Let's start by defining a non-autonomous ODE to evolve the states over the time period of interest:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define neural network of the NODE\n",
+    "fx = blocks.MLP(nx+nu, nx, bias=True,\n",
+    "                 linear_map=torch.nn.Linear,\n",
+    "                 nonlin=torch.nn.ReLU,\n",
+    "                 hsizes=[10, 10])\n",
+    "\n",
+    "fxRK4 = integrators.RK4(fx, h=1.0)\n",
+    "\n",
+    "dynamics_model = System([Node(fxRK4,['xn','U'],['xn'])])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need a loss function. Here, we use Neuromancer's built-in Variable abstraction to construct a loss that penalizes the misfit in flow rates and the height discrepancy between the two tanks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = variable(\"X\")\n",
+    "xhat = variable(\"xn\")[:, :-1, :]\n",
+    "reference_loss = ((xhat[:,:,[2,3]] == x[:,:,[2,3]])^2)\n",
+    "reference_loss.name = \"ref_loss\"\n",
+    "\n",
+    "height_loss = (1.0e0*(xhat[:,:,0] == xhat[:,:,1])^2)\n",
+    "height_loss.name = \"height_loss\"\n",
+    "\n",
+    "objectives = [reference_loss, height_loss]\n",
+    "constraints = []\n",
+    "# create constrained optimization loss\n",
+    "loss = PenaltyLoss(objectives, constraints)\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem([dynamics_model], loss)\n",
+    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.01)\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    problem,\n",
+    "    train_loader,\n",
+    "    dev_loader,\n",
+    "    test_loader,\n",
+    "    optimizer,\n",
+    "    epochs=10000,\n",
+    "    patience=20,\n",
+    "    warmup=50,\n",
+    "    eval_metric=\"dev_loss\",\n",
+    "    train_metric=\"train_loss\",\n",
+    "    dev_metric=\"dev_loss\",\n",
+    "    test_metric=\"dev_loss\",\n",
+    "    logger=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "epoch: 7  train_loss: 0.9612399339675903\n",
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+      "epoch: 202  train_loss: 8.874316699802876e-05\n",
+      "epoch: 203  train_loss: 8.874396735336632e-05\n",
+      "epoch: 204  train_loss: 8.874522609403357e-05\n",
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+      "epoch: 210  train_loss: 8.874307968653738e-05\n",
+      "epoch: 211  train_loss: 8.874270133674145e-05\n",
+      "epoch: 212  train_loss: 8.874307968653738e-05\n",
+      "epoch: 213  train_loss: 8.874358900357038e-05\n",
+      "epoch: 214  train_loss: 8.874294144334272e-05\n",
+      "epoch: 215  train_loss: 8.874341438058764e-05\n",
+      "epoch: 216  train_loss: 8.874377817846835e-05\n",
+      "epoch: 217  train_loss: 8.874342165654525e-05\n",
+      "epoch: 218  train_loss: 8.874296327121556e-05\n",
+      "epoch: 219  train_loss: 8.874281047610566e-05\n",
+      "epoch: 220  train_loss: 8.874309423845261e-05\n",
+      "epoch: 221  train_loss: 8.8743086962495e-05\n",
+      "epoch: 222  train_loss: 8.874292689142749e-05\n",
+      "epoch: 223  train_loss: 8.874353807186708e-05\n",
+      "epoch: 224  train_loss: 8.874333434505388e-05\n",
+      "epoch: 225  train_loss: 8.87430360307917e-05\n",
+      "epoch: 226  train_loss: 8.874317427398637e-05\n",
+      "epoch: 227  train_loss: 8.874261402525008e-05\n",
+      "epoch: 228  train_loss: 8.874257036950439e-05\n",
+      "epoch: 229  train_loss: 8.874297054717317e-05\n",
+      "epoch: 230  train_loss: 8.874307241057977e-05\n",
+      "epoch: 231  train_loss: 8.87427813722752e-05\n",
+      "epoch: 232  train_loss: 8.87433416210115e-05\n",
+      "epoch: 233  train_loss: 8.8743086962495e-05\n",
+      "epoch: 234  train_loss: 8.874262130120769e-05\n",
+      "epoch: 235  train_loss: 8.874305058270693e-05\n",
+      "epoch: 236  train_loss: 8.874277409631759e-05\n",
+      "epoch: 237  train_loss: 8.874285413185135e-05\n",
+      "epoch: 238  train_loss: 8.874276682035998e-05\n",
+      "epoch: 239  train_loss: 8.874299237504601e-05\n",
+      "epoch: 240  train_loss: 8.874270133674145e-05\n",
+      "epoch: 241  train_loss: 8.874289778759703e-05\n",
+      "epoch: 242  train_loss: 8.874331251718104e-05\n",
+      "epoch: 243  train_loss: 8.874275954440236e-05\n",
+      "epoch: 244  train_loss: 8.874350896803662e-05\n",
+      "epoch: 245  train_loss: 8.874270861269906e-05\n",
+      "epoch: 246  train_loss: 8.874290506355464e-05\n",
+      "epoch: 247  train_loss: 8.8743086962495e-05\n",
+      "epoch: 248  train_loss: 8.874261402525008e-05\n",
+      "Early stopping!!!\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_model = trainer.train()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make our lives easier, let's make a quick plotting function to use (we'll use this again later):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_and_plot(integrator):\n",
+    "\n",
+    "    # Roll out the model:\n",
+    "    end_step = len(data[:,0])\n",
+    "    sol = torch.zeros((end_step,5))\n",
+    "    sol[:,-1] = 0.5\n",
+    "    x0 = np.concatenate((data[0,:],U[0]))\n",
+    "    ic = torch.unsqueeze(torch.tensor(x0),0).float()\n",
+    "    t = 0\n",
+    "    for j in range(sol.shape[0]-1):\n",
+    "        if j==0:\n",
+    "            sol[[0],:] = ic\n",
+    "            sol[[j+1],:4] = integrator(sol[[0],:4],sol[[0],-1:])\n",
+    "        else:\n",
+    "            sol[[j+1],:4] = integrator(sol[[j],:4],sol[[j],-1:])\n",
+    "        t += time[1]-time[0]\n",
+    "\n",
+    "    # plot the results\n",
+    "    plt.plot(time,sol.detach().numpy()[:,0],label=\"Tank #1\")\n",
+    "    plt.plot(time,sol.detach().numpy()[:,1],label=\"Tank #2\")\n",
+    "    plt.plot(time,data[:,0],label=\"Data\",linestyle=\"--\")\n",
+    "    plt.xlabel(\"Time\")\n",
+    "    plt.ylabel(\"Height\")\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "\n",
+    "    plt.plot(time,sol.detach().numpy()[:,2],label=\"Inflow #1\")\n",
+    "    plt.plot(time,sol.detach().numpy()[:,3],label=\"Inflow #1\")\n",
+    "    plt.plot(time,np.sum(sol.detach().numpy()[:,[2,3]],-1),label=\"In_1 + In_2\")\n",
+    "    plt.plot(time,data[:,2],label=\"Data Inflow #1\",linestyle=\"--\")\n",
+    "    plt.plot(time,data[:,3],label=\"Data Inflow #2\",linestyle=\"--\")\n",
+    "\n",
+    "    plt.xlim([0,500])\n",
+    "    plt.ylim([0,0.6])\n",
+    "    plt.xlabel(\"Time\")\n",
+    "    plt.ylabel(\"Volumetric Flow\")\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "process_and_plot(fxRK4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Oh no! What happened? How come the results look very bad? In fact, is that a *negative height*? And those flow rates don't add up to the pump flow rate? We need to do something different."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Neural Differential Algebraic Equations**\n",
+    "\n",
+    "The constraints we saw in the problem setup should probably be obeyed - but how? The governing equations for the system are:\n",
+    "$$\n",
+    "\\frac{dh_1}{dt} = \\frac{1}{A_1 \\left( h_1 \\right)}q_1\n",
+    "$$\n",
+    "$$\n",
+    "\\frac{dh_2}{dt} = \\frac{1}{A_2 \\left( h_2 \\right)}q_2 . \n",
+    "$$\n",
+    "$$\n",
+    "0 = q_\\text{pump} - q_2 - q_2.\n",
+    "$$\n",
+    "$$\n",
+    "0 = h_1 - h_2\n",
+    "$$\n",
+    "The above equations constitute a Differential-Algebraic Equation; that is, a system of equations where some states evolve according to ODEs and others evolve according to algebraic relationships. Here, the heights of the tanks are *differential* variables and the flows $q_i$ are *algebraic* variables. We need an algorithm to handle these appropriately.\n",
+    "\n",
+    "In explicit timestepping for ODEs, we obtain a future state by recursive application of a rule. For example, in Forward Euler, we write:\n",
+    "$$\n",
+    "\\mathbf{x}\\left(t+\\Delta t\\right) = \\mathbf{x}(t) + \\Delta t f\\left( \\mathbf{x}(t)\\right) = \\text{ODESolve}\\left( f, \\mathbf{x}(t)), \\Delta t \\right),\n",
+    "$$\n",
+    "where $f$ is the right-hand-side of the ODE. This works fine for differential states, but we need an additional update rule for algebriac states. The simplest strategy is to add an algebraic variable update step to this relationship as:\n",
+    "$$\n",
+    "\\mathbf{y}\\left(t + \\Delta t\\right) = h\\left( \\mathbf{x}\\left(t\\right), \\mathbf{y}\\left(t\\right),\\mathbf{u}\\left(t\\right) \\right)\n",
+    "$$\n",
+    "$$\n",
+    "\\mathbf{x}\\left(t + \\Delta t\\right) = \\mathbf{x}\\left(t\\right) + \\Delta t \\cdot f\\left( \\mathbf{x}\\left(t\\right), \\mathbf{y}\\left(t + \\Delta t\\right),\\mathbf{u}\\left(t\\right) \\right),  \n",
+    "$$\n",
+    "where $\\mathbf{x}$ are differential states, $\\mathbf{y}$ are algebraic states, and $h$ is an update rule for algebraic states or surrogate thereof. Let's use this approach to re-do our modeling problem:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Definition of Update Rules:**\n",
+    "\n",
+    "First we need to define what the update rules should be. We'll use a couple of custom classes to define the evolution of differential states and algebraic states and how they might interact:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Class for 'black-box' differential state evolution\n",
+    "class BBNodeDiff(physics.Agent):\n",
+    "    def __init__(self, state_keys = None, in_keys = None, solver = None, profile = None):\n",
+    "        super().__init__(state_keys=state_keys)\n",
+    "        self.solver = solver\n",
+    "        self.in_keys = in_keys\n",
+    "        self.profile = profile\n",
+    "\n",
+    "    def intrinsic(self, x, y):\n",
+    "        # We want only positive values here; negative are nonphysical:\n",
+    "        return self.profile(x)\n",
+    "\n",
+    "    def algebra(self, x):\n",
+    "        return x[:,:len(self.state_keys)]\n",
+    "\n",
+    "# Class for 'black-box' algebraic state evolution\n",
+    "class BBNodeAlgebra(physics.Agent):\n",
+    "    def __init__(self, state_keys = None, in_keys = None, solver = None, profile = None):\n",
+    "        super().__init__(state_keys=state_keys)\n",
+    "        self.solver = solver\n",
+    "        self.in_keys = in_keys\n",
+    "        self.profile = profile\n",
+    "\n",
+    "    def intrinsic(self, x, y):\n",
+    "        return torch.zeros_like(x[:,:len(self.state_keys)])\n",
+    "\n",
+    "    def algebra(self, x):\n",
+    "        # Learning the convex combination of stream outputs that equal the input\n",
+    "        param = torch.abs(self.solver(x[:,1:]))\n",
+    "        return torch.cat((x[:,[0]]*param,x[:,[0]]*(1.0 - param)),-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we need to construct the mappings that these classes will use to update our states:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ode_rhs = blocks.MLP(insize=4, outsize=2, hsizes=[5],\n",
+    "                            linear_map=slim.maps['linear'],\n",
+    "                            nonlin=nn.LeakyReLU)\n",
+    "\n",
+    "algebra_solver_bb = blocks.MLP(insize=4, outsize=1, hsizes=[5],\n",
+    "                            linear_map=slim.maps['linear'],\n",
+    "                            nonlin=nn.LeakyReLU)\n",
+    "\n",
+    "# Define differential agent:\n",
+    "diff = BBNodeDiff(in_keys=[\"h_1\",\"h_2\",\"m_1\",\"m_2\"], state_keys=[\"h_1\",\"h_2\"], profile=ode_rhs)\n",
+    "\n",
+    "# Define algebraic agent:\n",
+    "alg = BBNodeAlgebra(in_keys = [\"m\",\"h_1\",\"h_2\",\"m_1\",\"m_2\"], state_keys=[\"m_1\",\"m_2\"], solver=algebra_solver_bb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we link these two objects (diff and alg) together in the GeneralNetworkedODE and GeneralNetworkedAE class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agents = [diff, alg]\n",
+    "\n",
+    "couplings = []\n",
+    "\n",
+    "model_ode = ode.GeneralNetworkedODE(\n",
+    "    states=states,\n",
+    "    agents=agents,\n",
+    "    couplings=couplings,\n",
+    "    insize=nx+nu,\n",
+    "    outsize=nx,\n",
+    ")\n",
+    "\n",
+    "model_algebra = ode.GeneralNetworkedAE(\n",
+    "    states=states,\n",
+    "    agents=agents,\n",
+    "    insize=nx+nu,\n",
+    "    outsize=nx ,\n",
+    ")\n",
+    "\n",
+    "fx_int = integrators.EulerDAE(model_ode,algebra=model_algebra,h=1.0)\n",
+    "dynamics_model = System([Node(fx_int,['xn','U'],['xn'])])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct constrained optimization problem\n",
+    "problem = Problem([dynamics_model], loss)\n",
+    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.005)\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    problem,\n",
+    "    train_loader,\n",
+    "    dev_loader,\n",
+    "    test_loader,\n",
+    "    optimizer,\n",
+    "    epochs=10000,\n",
+    "    patience=50,\n",
+    "    warmup=50,\n",
+    "    eval_metric=\"dev_loss\",\n",
+    "    train_metric=\"train_loss\",\n",
+    "    dev_metric=\"dev_loss\",\n",
+    "    test_metric=\"dev_loss\",\n",
+    "    logger=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Early stopping!!!\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_model = trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "process_and_plot(fx_int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's still not looking the best, but at least the two flows combine to equal the correct inlet volumetric flow. For this problem, since we know that the dynamics obey a certain form, a gray-box (or parameter tuning) approach may be best. Let's try that next.\n",
+    "\n",
+    "First, we need to instantiate a couple of Neural Networks: one to to approximate the missing tank area-height profile and another to act as a surrogate for an algebra solver:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tank area - height profiles: These should map height to area. R^1 -> R^1.\n",
+    "tank_profile = blocks.MLP(insize=1, outsize=1, hsizes=[3],\n",
+    "                            linear_map=slim.maps['linear'],\n",
+    "                            nonlin=nn.Sigmoid)\n",
+    "\n",
+    "# Surrogate for algebra solver: This should map 'algebraic state indices' to len(state names). \n",
+    "algebra_solver = blocks.MLP(insize=4, outsize=1, hsizes=[3],\n",
+    "                            linear_map=slim.maps['linear'],\n",
+    "                            nonlin=nn.Sigmoid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we need a few objects to describe the layout of the system. Becase we're now doing a gray-box model, we need to create objects of sufficient granularity/detail to evolve the height and mass flow state variables. We'll construct two `MIMOTank` objects, a pump object, a manifold object, and a set of 'pipes' that connect these objects together. Beginning with the tanks and pump, we expect these to evolve according to some pre-defined physics. For example, we know that the tanks have some area-height profile, and therefore the height scales with flow rate. This physics is built into the `MIMOTank` class. Likewise, the we need an object to anchor the exogenous input of the pump. We handle this with a `SourceSink` object - this class does not evolve states, but serves to correctly account for the integration of the exogenous input into this networked system. Note that these objects take as arguments *in_keys* and *state_keys* as arguments. Each object modifies its *state_keys* based on a forward pass that takes as input *in_keys*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Individual components:\n",
+    "tank_1 = physics.MIMOTank(state_keys=[\"h_1\"], in_keys=[\"h_1\"], profile= lambda x: 3.0) # assume known area-height profile\n",
+    "tank_2 = physics.MIMOTank(state_keys=[\"h_2\"], in_keys=[\"h_2\"], profile=tank_profile)\n",
+    "pump = physics.SourceSink(state_keys=[\"m\"], in_keys=[\"m\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The manifold acts to split the flow of water from the pump into the two streams. This is an algebraic relationship - we need to handle this accordingly in the gray-box model. The `SIMOConservationNode` class splits a single input into multiple outputs according to the surrogate algebra solver (a neural network in this case):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define algebraic agent:\n",
+    "manifold = physics.SIMOConservationNode(in_keys = [\"m\",\"h_1\",\"h_2\",\"m_1\",\"m_2\"], state_keys=[\"m_1\",\"m_2\"], solver=algebra_solver)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As with other networked systems, we combine these agents into a list for later:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Accumulate agents in list:\n",
+    "# index:   0       1        2       3 \n",
+    "agents = [pump, tank_1, tank_2, manifold]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The different agents are connected via `Pipe` couplings. Just like real pipes in a fluid system, these transport flows from one node in the network to another. Here, we have three fluid streams that need to be defined: (i) from the pump to the manifold, (ii) from the manifld to tank #1, and (iii) from the manifold to tank #2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "couplings = []\n",
+    "# Couple w/ pipes:\n",
+    "couplings.append(physics.Pipe(in_keys = [\"m\"], pins = [[0,3]])) # Pump -> Manifold\n",
+    "couplings.append(physics.Pipe(in_keys = [\"m_1\"], pins = [[3,1]])) # Manifold -> tank_1\n",
+    "couplings.append(physics.Pipe(in_keys = [\"m_2\"], pins = [[3,2]])) # Manifold -> tank_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The agents and couplings are aggregated via the `GeneralNetworkedODE` and `GeneralNetworkedAE` classes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "model_ode = ode.GeneralNetworkedODE(\n",
+    "    states=states,\n",
+    "    agents=agents,\n",
+    "    couplings=couplings,\n",
+    "    insize=nx+nu,\n",
+    "    outsize=nx,\n",
+    ")\n",
+    "\n",
+    "model_algebra = ode.GeneralNetworkedAE(\n",
+    "    states=states,\n",
+    "    agents=agents,\n",
+    "    insize=nx+nu,\n",
+    "    outsize=nx,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can put all of the pieces together to form a complete model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the new timestepper:\n",
+    "fx_dae = integrators.EulerDAE(model_ode,algebra=model_algebra,h=1.0)\n",
+    "dynamics_model = System([Node(fx_dae,['xn','U'],['xn'])])\n",
+    "\n",
+    "# construct constrained optimization problem\n",
+    "problem = Problem([dynamics_model], loss)\n",
+    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.005)\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    problem,\n",
+    "    train_loader,\n",
+    "    dev_loader,\n",
+    "    test_loader,\n",
+    "    optimizer,\n",
+    "    epochs=10000,\n",
+    "    patience=50,\n",
+    "    warmup=50,\n",
+    "    eval_metric=\"dev_loss\",\n",
+    "    train_metric=\"train_loss\",\n",
+    "    dev_metric=\"dev_loss\",\n",
+    "    test_metric=\"dev_loss\",\n",
+    "    logger=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "epoch: 9941  train_loss: 1.4854355868010316e-05\n",
+      "epoch: 9942  train_loss: 1.4499386452371255e-05\n",
+      "epoch: 9943  train_loss: 1.428017822036054e-05\n",
+      "epoch: 9944  train_loss: 1.4305156582850032e-05\n",
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+      "epoch: 9952  train_loss: 1.430116481060395e-05\n",
+      "epoch: 9953  train_loss: 1.4357394320541061e-05\n",
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+      "epoch: 9958  train_loss: 1.4124570952844806e-05\n",
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+      "epoch: 9960  train_loss: 1.418299234501319e-05\n",
+      "epoch: 9961  train_loss: 1.4166075743560214e-05\n",
+      "epoch: 9962  train_loss: 1.4118856597633567e-05\n",
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+      "epoch: 9965  train_loss: 1.4031822502147406e-05\n",
+      "epoch: 9966  train_loss: 1.4042283510207199e-05\n",
+      "epoch: 9967  train_loss: 1.4048206139705144e-05\n",
+      "epoch: 9968  train_loss: 1.4039550478628371e-05\n",
+      "epoch: 9969  train_loss: 1.4014824955665972e-05\n",
+      "epoch: 9970  train_loss: 1.398620406689588e-05\n",
+      "epoch: 9971  train_loss: 1.3963489436719101e-05\n",
+      "epoch: 9972  train_loss: 1.395008439430967e-05\n",
+      "epoch: 9973  train_loss: 1.3946675608167425e-05\n",
+      "epoch: 9974  train_loss: 1.3945413229521364e-05\n",
+      "epoch: 9975  train_loss: 1.3940586541139055e-05\n",
+      "epoch: 9976  train_loss: 1.3927755389886443e-05\n",
+      "epoch: 9977  train_loss: 1.390975830872776e-05\n",
+      "epoch: 9978  train_loss: 1.3890600712329615e-05\n",
+      "epoch: 9979  train_loss: 1.3874744581698906e-05\n",
+      "epoch: 9980  train_loss: 1.3863626008969732e-05\n",
+      "epoch: 9981  train_loss: 1.3855630641046446e-05\n",
+      "epoch: 9982  train_loss: 1.3849402421328705e-05\n",
+      "epoch: 9983  train_loss: 1.3840331121173222e-05\n",
+      "epoch: 9984  train_loss: 1.3829027921019588e-05\n",
+      "epoch: 9985  train_loss: 1.3814764315611683e-05\n",
+      "epoch: 9986  train_loss: 1.3801504792354535e-05\n",
+      "epoch: 9987  train_loss: 1.3789223885396495e-05\n",
+      "epoch: 9988  train_loss: 1.377736589347478e-05\n",
+      "epoch: 9989  train_loss: 1.3766678421234246e-05\n",
+      "epoch: 9990  train_loss: 1.3758347449766006e-05\n",
+      "epoch: 9991  train_loss: 1.3748336641583592e-05\n",
+      "epoch: 9992  train_loss: 1.3739103451371193e-05\n",
+      "epoch: 9993  train_loss: 1.3727727491641417e-05\n",
+      "epoch: 9994  train_loss: 1.3715713066630997e-05\n",
+      "epoch: 9995  train_loss: 1.3704430784855504e-05\n",
+      "epoch: 9996  train_loss: 1.369291749142576e-05\n",
+      "epoch: 9997  train_loss: 1.3681632481166162e-05\n",
+      "epoch: 9998  train_loss: 1.3670921362063382e-05\n",
+      "epoch: 9999  train_loss: 1.3660293916473165e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_model = trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "process_and_plot(fx_dae)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nm14",
+   "language": "python",
+   "name": "nm14"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/ODEs/Part_7_DAE.py b/examples/ODEs/Part_7_DAE.py
new file mode 100644
index 00000000..0c46d2a4
--- /dev/null
+++ b/examples/ODEs/Part_7_DAE.py
@@ -0,0 +1,337 @@
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import os
+
+import neuromancer.slim as slim
+from neuromancer.modules import blocks, activations
+from neuromancer.dynamics import integrators, ode, physics
+from neuromancer.trainer import Trainer
+from neuromancer.problem import Problem
+from neuromancer.dataset import DictDataset
+from neuromancer.loss import PenaltyLoss
+from neuromancer.constraint import variable, Objective
+from neuromancer.system import Node, System
+from neuromancer.loggers import BasicLogger
+
+from collections import OrderedDict
+from abc import ABC, abstractmethod
+
+torch.manual_seed(0)
+device = 'cpu'
+
+plt.rcParams["font.family"] = "serif"
+#plt.rcParams["font.serif"] = ["Times"]
+plt.rcParams['figure.dpi'] = 300
+plt.rcParams.update({'font.size': 10})
+
+params = {'legend.fontsize': 10,
+         'axes.labelsize': 10,
+         'axes.titlesize': 10,
+         'xtick.labelsize': 10,
+         'ytick.labelsize': 10}
+plt.rcParams.update(params)
+
+data = np.float32(np.loadtxt('data/tanks.dat'))
+data=data[1:497,]
+area_data = np.loadtxt('data/area.dat')
+time = np.float32(np.linspace(0.0,len(data[:,0])-1,len(data[:,0])).reshape(-1, 1))
+U = time*0.0 + 0.5
+
+train_data = {'Y': data[1:], 'X': data[1:], 'Time': time[1:], 'U': U[1:] }
+dev_data = train_data
+test_data = train_data
+
+nsim = data.shape[0]
+nx = data.shape[1]
+nstep = 15
+
+for d in [train_data, dev_data]:
+    d['X'] = d['X'].reshape(nsim//nstep, nstep, nx)
+    d['Y'] = d['Y'].reshape(nsim//nstep, nstep, nx)
+    d['xn'] = d['X'][:, 0:1, :] # Add an initial condition to start the system loop
+    d['Time'] = d['Time'].reshape(nsim//nstep, nstep, 1)
+    d['U'] = d['U'].reshape(nsim//nstep, nstep, 1)
+
+train_dataset, dev_dataset, = [DictDataset(d, name=n) for d, n in zip([train_data, dev_data], ['train', 'dev'])]
+train_loader, dev_loader, test_loader = [DataLoader(d, batch_size=nsim//nstep, collate_fn=d.collate_fn, shuffle=True) for d in [train_dataset, dev_dataset, dev_dataset]]
+
+nx = 4 # set the state dimension
+nu = 1 # set the exogenous input dimension
+
+# State names if we need them (we do)
+states = {}
+states['h_1'] = 0
+states['h_2'] = 1
+states['m_1'] = 2
+states['m_2'] = 3
+states['m'] = 4
+
+plt.plot(time,data[:,0])
+plt.xlim([0,500])
+plt.ylim([0,40])
+plt.xlabel("Time")
+plt.ylabel("Height")
+plt.show()
+
+plt.plot(time,data[:,[2,3]])
+plt.plot(time,data[:,2]+data[:,3])
+plt.xlim([0,500])
+plt.ylim([0,0.6])
+plt.xlabel("Time")
+plt.ylabel("Volumetric Flow")
+plt.show()
+
+############### Black-box Neural ODE Model ###############
+#  define neural network of the NODE
+fx = blocks.MLP(nx+nu, nx, bias=True,
+                 linear_map=torch.nn.Linear,
+                 nonlin=torch.nn.ReLU,
+                 hsizes=[10, 10])
+
+fxRK4 = integrators.RK4(fx, h=1.0)
+
+dynamics_model = System([Node(fxRK4,['xn','U'],['xn'])])
+
+x = variable("X")
+xhat = variable("xn")[:, :-1, :]
+reference_loss = ((xhat[:,:,[2,3]] == x[:,:,[2,3]])^2)
+reference_loss.name = "ref_loss"
+
+height_loss = (1.0e0*(xhat[:,:,0] == xhat[:,:,1])^2)
+height_loss.name = "height_loss"
+
+objectives = [reference_loss, height_loss]
+constraints = []
+# create constrained optimization loss
+loss = PenaltyLoss(objectives, constraints)
+# construct constrained optimization problem
+problem = Problem([dynamics_model], loss)
+
+optimizer = torch.optim.Adam(problem.parameters(), lr=0.01)
+
+trainer = Trainer(
+    problem,
+    train_loader,
+    dev_loader,
+    test_loader,
+    optimizer,
+    epochs=10000,
+    patience=20,
+    warmup=50,
+    eval_metric="dev_loss",
+    train_metric="train_loss",
+    dev_metric="dev_loss",
+    test_metric="dev_loss",
+    logger=None,
+)
+
+best_model = trainer.train()
+
+def process_and_plot(integrator):
+
+    # Roll out the model:
+    end_step = len(data[:,0])
+    sol = torch.zeros((end_step,5))
+    sol[:,-1] = 0.5
+    x0 = np.concatenate((data[0,:],U[0]))
+    ic = torch.unsqueeze(torch.tensor(x0),0).float()
+    t = 0
+    for j in range(sol.shape[0]-1):
+        if j==0:
+            sol[[0],:] = ic
+            sol[[j+1],:4] = integrator(sol[[0],:4],sol[[0],-1:])
+        else:
+            sol[[j+1],:4] = integrator(sol[[j],:4],sol[[j],-1:])
+        t += time[1]-time[0]
+
+    # plot the results
+    plt.plot(time,sol.detach().numpy()[:,0],label="Tank #1")
+    plt.plot(time,sol.detach().numpy()[:,1],label="Tank #2")
+    plt.plot(time,data[:,0],label="Data",linestyle="--")
+    plt.xlabel("Time")
+    plt.ylabel("Height")
+    plt.legend()
+    plt.show()
+
+    plt.plot(time,sol.detach().numpy()[:,2],label="Inflow #1")
+    plt.plot(time,sol.detach().numpy()[:,3],label="Inflow #1")
+    plt.plot(time,np.sum(sol.detach().numpy()[:,[2,3]],-1),label="In_1 + In_2")
+    plt.plot(time,data[:,2],label="Data Inflow #1",linestyle="--")
+    plt.plot(time,data[:,3],label="Data Inflow #2",linestyle="--")
+
+    plt.xlim([0,500])
+    plt.ylim([0,0.6])
+    plt.xlabel("Time")
+    plt.ylabel("Volumetric Flow")
+    plt.legend()
+    plt.show()
+
+process_and_plot(fxRK4)
+
+############### Black-box Neural DAE Model ############### 
+
+# Class for 'black-box' differential state evolution
+class BBNodeDiff(physics.Agent):
+    def __init__(self, state_keys = None, in_keys = None, solver = None, profile = None):
+        super().__init__(state_keys=state_keys)
+        self.solver = solver
+        self.in_keys = in_keys
+        self.profile = profile
+
+    def intrinsic(self, x, y):
+        return self.profile(x)
+
+    def algebra(self, x):
+        return x[:,:len(self.state_keys)]
+
+# Class for 'black-box' algebraic state evolution
+class BBNodeAlgebra(physics.Agent):
+    def __init__(self, state_keys = None, in_keys = None, solver = None, profile = None):
+        super().__init__(state_keys=state_keys)
+        self.solver = solver
+        self.in_keys = in_keys
+        self.profile = profile
+
+    def intrinsic(self, x, y):
+        return torch.zeros_like(x[:,:len(self.state_keys)])
+
+    def algebra(self, x):
+        # Learning the convex combination of stream outputs that equal the input
+        param = torch.abs(self.solver(x[:,1:]))
+        return torch.cat((x[:,[0]]*param,x[:,[0]]*(1.0 - param)),-1)
+
+ode_rhs = blocks.MLP(insize=4, outsize=2, hsizes=[5],
+                            linear_map=slim.maps['linear'],
+                            nonlin=nn.LeakyReLU)
+
+algebra_solver_bb = blocks.MLP(insize=4, outsize=1, hsizes=[5],
+                            linear_map=slim.maps['linear'],
+                            nonlin=nn.LeakyReLU)
+
+# Define differential agent:
+diff = BBNodeDiff(in_keys=["h_1","h_2","m_1","m_2"], state_keys=["h_1","h_2"], profile=ode_rhs)
+
+# Define algebraic agent:
+alg = BBNodeAlgebra(in_keys = ["m","h_1","h_2","m_1","m_2"], state_keys=["m_1","m_2"], solver=algebra_solver_bb)
+
+agents = [diff, alg]
+
+couplings = []
+
+model_ode = ode.GeneralNetworkedODE(
+    states=states,
+    agents=agents,
+    couplings=couplings,
+    insize=nx+nu,
+    outsize=nx,
+)
+
+model_algebra = ode.GeneralNetworkedAE(
+    states=states,
+    agents=agents,
+    insize=nx+nu,
+    outsize=nx ,
+)
+
+fx_int = integrators.EulerDAE(model_ode,algebra=model_algebra,h=1.0)
+dynamics_model = System([Node(fx_int,['xn','U'],['xn'])])
+
+# construct constrained optimization problem
+problem = Problem([dynamics_model], loss)
+optimizer = torch.optim.Adam(problem.parameters(), lr=0.005)
+
+trainer = Trainer(
+    problem,
+    train_loader,
+    dev_loader,
+    test_loader,
+    optimizer,
+    epochs=10000,
+    patience=50,
+    warmup=50,
+    eval_metric="dev_loss",
+    train_metric="train_loss",
+    dev_metric="dev_loss",
+    test_metric="dev_loss",
+    logger=None,
+)
+
+best_model = trainer.train()
+process_and_plot(fx_int)
+
+############### Gray-box DAE Model ###############
+
+# Tank area - height profiles: These should map height to area. R^1 -> R^1.
+tank_profile = blocks.MLP(insize=1, outsize=1, hsizes=[3],
+                            linear_map=slim.maps['linear'],
+                            nonlin=nn.Sigmoid)
+
+# Surrogate for algebra solver: This should map 'algebraic state indices' to len(state names). 
+algebra_solver = blocks.MLP(insize=4, outsize=1, hsizes=[3],
+                            linear_map=slim.maps['linear'],
+                            nonlin=nn.Sigmoid)
+                            
+# Individual components:
+tank_1 = physics.MIMOTank(state_keys=["h_1"], in_keys=["h_1"], profile= lambda x: 3.0) # assume known area-height profile
+tank_2 = physics.MIMOTank(state_keys=["h_2"], in_keys=["h_2"], profile=tank_profile)
+pump = physics.SourceSink(state_keys=["m"], in_keys=["m"])
+
+# Define algebraic agent:
+manifold = physics.SIMOConservationNode(in_keys = ["m","h_1","h_2","m_1","m_2"], state_keys=["m_1","m_2"], solver=algebra_solver)
+
+# Accumulate agents in list:
+# index:   0       1        2       3 
+agents = [pump, tank_1, tank_2, manifold]
+
+couplings = []
+# Couple w/ pipes:
+couplings.append(physics.Pipe(in_keys = ["m"], pins = [[0,3]])) # Pump -> Manifold
+couplings.append(physics.Pipe(in_keys = ["m_1"], pins = [[3,1]])) # Manifold -> tank_1
+couplings.append(physics.Pipe(in_keys = ["m_2"], pins = [[3,2]])) # Manifold -> tank_2
+
+model_ode = ode.GeneralNetworkedODE(
+    states=states,
+    agents=agents,
+    couplings=couplings,
+    insize=nx+nu,
+    outsize=nx,
+)
+
+model_algebra = ode.GeneralNetworkedAE(
+    states=states,
+    agents=agents,
+    insize=nx+nu,
+    outsize=nx,
+)
+
+fx_dae = integrators.EulerDAE(model_ode,algebra=model_algebra,h=1.0)
+dynamics_model = System([Node(fx_dae,['xn','U'],['xn'])])
+
+# construct constrained optimization problem
+problem = Problem([dynamics_model], loss)
+optimizer = torch.optim.Adam(problem.parameters(), lr=0.005)
+
+trainer = Trainer(
+    problem,
+    train_loader,
+    dev_loader,
+    test_loader,
+    optimizer,
+    epochs=10000,
+    patience=50,
+    warmup=50,
+    eval_metric="dev_loss",
+    train_metric="train_loss",
+    dev_metric="dev_loss",
+    test_metric="dev_loss",
+    logger=None,
+)
+
+best_model = trainer.train()
+process_and_plot(fx_dae)
+
+# %%
diff --git a/examples/ODEs/data/area.dat b/examples/ODEs/data/area.dat
new file mode 100644
index 00000000..6e39e86c
--- /dev/null
+++ b/examples/ODEs/data/area.dat
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diff --git a/examples/ODEs/data/tanks.dat b/examples/ODEs/data/tanks.dat
new file mode 100644
index 00000000..56ccdde5
--- /dev/null
+++ b/examples/ODEs/data/tanks.dat
@@ -0,0 +1,501 @@
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diff --git a/examples/ODEs/figs/manifold.png b/examples/ODEs/figs/manifold.png
new file mode 100644
index 00000000..7e15299e
Binary files /dev/null and b/examples/ODEs/figs/manifold.png differ
diff --git a/examples/tutorials/part_3_node.ipynb b/examples/tutorials/part_3_node.ipynb
index 2d9cbc2b..8baa8166 100644
--- a/examples/tutorials/part_3_node.ipynb
+++ b/examples/tutorials/part_3_node.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "5e418a0c",
    "metadata": {},
    "outputs": [],
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "26f20c73",
    "metadata": {},
    "outputs": [],
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "32bde295",
    "metadata": {},
    "outputs": [
@@ -76,7 +76,7 @@
      "text": [
       "['x1']\n",
       "['y1']\n",
-      "{'y1': tensor([-0.6689], grad_fn=<AddBackward0>)}\n"
+      "{'y1': tensor([1.1393], grad_fn=<AddBackward0>)}\n"
      ]
     }
    ],
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "41bf103b",
    "metadata": {},
    "outputs": [
@@ -101,7 +101,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'y2': tensor([-0.3779], grad_fn=<AddBackward0>)}\n"
+      "{'y2': tensor([0.3815], grad_fn=<AddBackward0>)}\n"
      ]
     }
    ],
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "f5390740",
    "metadata": {},
    "outputs": [
@@ -127,7 +127,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'y3': tensor([0.1539, 0.3889])}\n"
+      "{'y3': tensor([0.5863, 1.0293])}\n"
      ]
     }
    ],
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "7c9667a1",
    "metadata": {},
    "outputs": [
@@ -149,7 +149,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'x1^2': tensor([0.0555, 0.5139]), 'x2^2': tensor([0.2789, 0.3003])}\n"
+      "{'x1^2': tensor([0.5003, 0.0016]), 'x2^2': tensor([0.4650, 0.5566])}\n"
      ]
     }
    ],
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "9bbaee06",
    "metadata": {},
    "outputs": [],
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "0817c98f",
    "metadata": {},
    "outputs": [
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "06e573e6",
    "metadata": {},
    "outputs": [
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "274fef37",
    "metadata": {},
    "outputs": [
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "13555800",
    "metadata": {},
    "outputs": [
@@ -298,39 +298,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['x1', 'x2']\n",
-      "['y1', 'y2', 'y3']\n",
-      "{'x1': tensor([[[0.8118],\n",
-      "         [0.9097],\n",
-      "         [0.7591]],\n",
+      "['x2', 'x1']\n",
+      "['y2', 'y3', 'y1']\n",
+      "{'x1': tensor([[[0.9775],\n",
+      "         [0.1540],\n",
+      "         [0.1930]],\n",
       "\n",
-      "        [[0.1526],\n",
-      "         [0.8383],\n",
-      "         [0.1637]]]), 'x2': tensor([[[0.3108, 0.3442],\n",
-      "         [0.8555, 0.9287],\n",
-      "         [0.4466, 0.6320]],\n",
+      "        [[0.7350],\n",
+      "         [0.2193],\n",
+      "         [0.9826]]]), 'x2': tensor([[[0.9946, 0.0532],\n",
+      "         [0.7876, 0.0134],\n",
+      "         [0.4387, 0.1852]],\n",
       "\n",
-      "        [[0.7797, 0.3811],\n",
-      "         [0.1139, 0.8550],\n",
-      "         [0.7192, 0.5203]]]), 'y1': tensor([[[-0.9818],\n",
-      "         [-1.0222],\n",
-      "         [-0.9601]],\n",
+      "        [[0.7090, 0.1308],\n",
+      "         [0.2429, 0.6598],\n",
+      "         [0.3901, 0.7855]]]), 'y1': tensor([[[1.7805],\n",
+      "         [1.1182],\n",
+      "         [1.1496]],\n",
       "\n",
-      "        [[-0.7099],\n",
-      "         [-0.9928],\n",
-      "         [-0.7145]]], grad_fn=<CatBackward0>), 'y2': tensor([[[-0.3476],\n",
-      "         [-0.4056],\n",
-      "         [-0.3570]],\n",
+      "        [[1.5855],\n",
+      "         [1.1707],\n",
+      "         [1.7845]]], grad_fn=<CatBackward0>), 'y2': tensor([[[0.3939],\n",
+      "         [0.3844],\n",
+      "         [0.3815]],\n",
       "\n",
-      "        [[-0.3911],\n",
-      "         [-0.3232],\n",
-      "         [-0.3838]]], grad_fn=<CatBackward0>), 'y3': tensor([[[-2.0845],\n",
-      "         [-2.2089],\n",
-      "         [-2.0476]],\n",
+      "        [[0.3815],\n",
+      "         [0.3815],\n",
+      "         [0.3815]]], grad_fn=<CatBackward0>), 'y3': tensor([[[3.4058],\n",
+      "         [2.0887],\n",
+      "         [2.1537]],\n",
       "\n",
-      "        [[-1.5727],\n",
-      "         [-2.0900],\n",
-      "         [-1.5762]]], grad_fn=<CatBackward0>)}\n"
+      "        [[3.0255],\n",
+      "         [2.1960],\n",
+      "         [3.4236]]], grad_fn=<CatBackward0>)}\n"
      ]
     }
    ],
@@ -353,10 +353,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "208a9928",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# visualize symbolic computational graph\n",
     "system_1.show()"
@@ -364,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "2dbe87c4",
    "metadata": {},
    "outputs": [
@@ -372,39 +383,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['y1', 'x1', 'x2']\n",
-      "['y1', 'y2']\n",
-      "{'x1': tensor([[[0.1347],\n",
-      "         [0.8139],\n",
-      "         [0.4466]],\n",
+      "['x2', 'x1', 'y1']\n",
+      "['y2', 'y1']\n",
+      "{'x1': tensor([[[0.2723],\n",
+      "         [0.1818],\n",
+      "         [0.4174]],\n",
       "\n",
-      "        [[0.9357],\n",
-      "         [0.6498],\n",
-      "         [0.0123]]]), 'x2': tensor([[[0.2463, 0.3173],\n",
-      "         [0.0356, 0.2024],\n",
-      "         [0.6412, 0.8192]],\n",
+      "        [[0.2056],\n",
+      "         [0.5259],\n",
+      "         [0.7163]]]), 'x2': tensor([[[0.6242, 0.3582],\n",
+      "         [0.9334, 0.0073],\n",
+      "         [0.6697, 0.0809]],\n",
       "\n",
-      "        [[0.8419, 0.6462],\n",
-      "         [0.4614, 0.4508],\n",
-      "         [0.6196, 0.5821]]]), 'y1': tensor([[[-0.7025],\n",
-      "         [-1.5219],\n",
-      "         [-0.9827],\n",
-      "         [-3.1484],\n",
-      "         [-0.8312],\n",
-      "         [-2.1093]],\n",
+      "        [[0.7018, 0.6212],\n",
+      "         [0.6515, 0.9655],\n",
+      "         [0.5936, 0.8272]]]), 'y1': tensor([[[1.2134],\n",
+      "         [2.2812],\n",
+      "         [1.1406],\n",
+      "         [4.4091],\n",
+      "         [1.3301],\n",
+      "         [2.1358]],\n",
       "\n",
-      "        [[-1.0330],\n",
-      "         [-2.2253],\n",
-      "         [-0.9150],\n",
-      "         [-4.5804],\n",
-      "         [-0.6520],\n",
-      "         [-1.9697]]], grad_fn=<CatBackward0>), 'y2': tensor([[[-0.3419],\n",
-      "         [-0.3235],\n",
-      "         [-0.3794]],\n",
+      "        [[1.1598],\n",
+      "         [2.1752],\n",
+      "         [1.4173],\n",
+      "         [4.2057],\n",
+      "         [1.5705],\n",
+      "         [2.6902]]], grad_fn=<CatBackward0>), 'y2': tensor([[[0.3815],\n",
+      "         [0.3916],\n",
+      "         [0.3815]],\n",
       "\n",
-      "        [[-0.3992],\n",
-      "         [-0.3605],\n",
-      "         [-0.3737]]], grad_fn=<CatBackward0>)}\n"
+      "        [[0.3799],\n",
+      "         [0.3805],\n",
+      "         [0.3800]]], grad_fn=<CatBackward0>)}\n"
      ]
     }
    ],
@@ -423,10 +434,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "f87c70a3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# visualize symbolic computational graph\n",
     "system_2.show()"
@@ -451,9 +473,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neuromancer",
+   "display_name": "nm14",
    "language": "python",
-   "name": "neuromancer"
+   "name": "nm14"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/neuromancer/dynamics/integrators.py b/src/neuromancer/dynamics/integrators.py
index c22c7268..febd5765 100644
--- a/src/neuromancer/dynamics/integrators.py
+++ b/src/neuromancer/dynamics/integrators.py
@@ -97,7 +97,35 @@ def integrate(self, x, *args):
         k1 = self.block(*[x, *args])        # k1 = f(x_i, t_i)
         return x + h*k1
 
+class EulerDAE(Integrator):
+    def __init__(self, block, algebra=None, interp_u=None, h=1.0):
+        """
+
+        :param block: (nn.Module) A state transition model.
+        :param algebra: (nn.Module) Model for evolving algebraic states.
+        :param interp_u: Function for interpolating control input values for intermediate integration steps.
+                         If you assume a constant control sequence over the time intervals of the samples then
+                         lambda u, t: u will work.
+                         See interpolation.py and neuromancer/examples/system_identifcation/duffing_parameter.py for
+                         more sophisticated interpolation schemes.
+        :param h: (float) integration step size
+        """
+        super().__init__(block=block, interp_u=interp_u, h=h)
+        self.algebra = algebra
+
+    def integrate(self, x, *args):
+        """_summary_
 
+        :param x: (torch.Tensor, shape=[batchsize, SysDim])
+        :param u: (torch.Tensor, shape=[batchsize, nu])
+        :param t: (torch.Tensor, shape=[batchsize, 1])
+        :return x_{t+1}: (torch.Tensor, shape=[batchsize, SysDim])
+        """
+        x = self.algebra(x, *args)
+        h = self.h
+        k1 = self.block(x, *args)
+        return x + h*k1
+    
 class Euler_Trap(Integrator):
     def __init__(self, block, interp_u=None, h=1.0):
         """
diff --git a/src/neuromancer/dynamics/ode.py b/src/neuromancer/dynamics/ode.py
index d516d780..b5398ee4 100644
--- a/src/neuromancer/dynamics/ode.py
+++ b/src/neuromancer/dynamics/ode.py
@@ -70,90 +70,139 @@ def forward(self, x, *args):
         assert len(x.shape) == 2
         return self.ode_equations(x, *args)
 
-
 class GeneralNetworkedODE(ODESystem):
-    """
-    Coupled nonlinear dynamical system with heterogeneous agents. This class acts as an
-    aggregator for multiple interacting physics that contribute to the dynamics of one
-    or more agents. 
-    """
+    """ Coupled nonlinear dynamical system with heterogeneous agents. Can be used standalone for networked ODE systems with
+    homo/heterogeneous agents or together with :class:'GeneralNetworkedAE' for the specification of differential-algebraic
+    equations."""
 
-    def __init__(self, map = None, 
+    def __init__(self, states = None, 
                 agents = None, 
                 couplings = None,
                 insize = None,
                 outsize = None,
-                inductive_bias = "additive"
                 ):
+        """Constructor method.
+        :param states: (dict) dictionary of state and index pairs, defaults to None
+        :param agents: (list(Agents)) list of agents for the networked system, defaults to None
+        :param couplings: (list(Couplings)) list of couplings between agents
+        :param insize: (int) in state dimension, defaults to None
+        :param outsize: (int) out state dimension, defaults to None
         """
-        :param map: mapping between state index and agent state name(s)
-        :param agents: list of ordered dicts, one per agent.
-        :param couplings: list of blocks. one per interaction type.
-        :param insize: dimensionality of input, including disturbances and control
-        :param outsize: dimensionality of output, just for agent evolution
-        :param inductive_bias: selection of inductive bias for ODE. additive or compositional
-        """
+
         super().__init__(insize=insize, outsize=outsize)
         
         # Composition of network:
-        self.map = map
+        self.states = states
         self.agents = nn.ModuleList(agents)
         self.couplings = nn.ModuleList(couplings)
         self.insize = insize
         self.outsize = outsize
-        self.inductive_bias = inductive_bias
  
-        assert len(self.map) == len(self.agents)
-
     def ode_equations(self, x, *args):
+        """Forward pass of the method. 
+
+        :param x: (torch.Tensor) input tensor of size (batches,states)
+        :return: (torch.Tensor) output tensor of size (batches,states)
         """
-        Select the inductive bias to use for the problem:
-         - Additive: f(x_i) + sum(g(x_i,x_j))
-         - General: f(x_i, sum(g(x_i,x_j)))
-         - Composed: f(sum(g(x_i,x_j)))
-        """
-        if self.inductive_bias == "additive":
-            dx = self.intrinsic_physics(x, *args) + self.coupling_physics(x, *args)
-        elif self.inductive_bias == "general":
-            #dx = self.intrinsic_physics(x,self.coupling_physics(x))
-            raise Exception("General RHS not implemented.")
-        elif self.inductive_bias == "compositional":
-            dx = self.intrinsic_physics(self.coupling_physics(x, *args), *args)
-        else:
-            raise Exception("No inductive bias match.")
-
-        return dx[:, :self.outsize]
-
-    def intrinsic_physics(self, x, *args):
-        """
-        Calculate and return the contribution from all agents' intrinsic physics
+
+        # construct the RHS of the ODE via physics(states,accumulated interactions)[return only autonomous part]
+        # dx/dt = f(x,\sum_A(couplings))
+        return self.intrinsic_physics(x,self.coupling_physics(x, *args), *args)[:,:self.outsize]
+
+    def intrinsic_physics(self, x, interactions, *args):
+        """Calculation of intrinsic physics contributions from agents. For each agent, 
+        aggregate interactions and call the forward pass of the agent.
+
+        :param x: (torch.Tensor) input tensor of size (batches,states), these are the states of the system
+        :param interactions: (torch.Tensor) input tensor of size (batches,states), these are the accumulated interactions of the system
+        :return: (torch.Tensor) output tensor of size (batches,states)
         """
-        dx = torch.tensor([])  # initialize empty to avoid indexing tedium
         features = torch.cat([x, *args], dim=-1)
-        # loop over agents and calculate contribution from intrinsic physics
-        for idx, agent_dict in enumerate(self.map):
-            dx = torch.cat((dx, self.agents[idx](features[:, list(agent_dict.values())])), -1)
-        return dx
+        dx = torch.zeros_like(features)
+
+        for agent in self.agents:
+            """Get all of the relevant attributes from the agent object and pass them as arguments to agents() fwd pass. 
+            Loop through each agent. construct RHS of the ODE.
+            """
 
+            state_idxs = list(map(self.states.get, agent.state_keys))
+            input_idxs = list(map(self.states.get, agent.in_keys))
+            dx[:,state_idxs] = agent(features[:,input_idxs], interactions[:,state_idxs])
+
+        return dx
+    
     def coupling_physics(self, x, *args):
+        """Calculate aggregated coupling physics across all connections in self.couplings.
+
+        :param x: (torch.Tensor) input tensor of size (batches,states), these are the states of the system
+        :return: (torch.Tensor) output tensor of size (batches,states)
         """
-        This coupling physics assumes that each coupling physics nn.Module contains the
-        connection information, including what agents are connected and if the connection 
-        is symmetric.
-        """
-        dx = torch.zeros_like(x)
+
         features = torch.cat([x, *args], dim=-1)
+        dx = torch.zeros_like(features)
+        
         # first loop over coupling physics listed in self.couplings
         for physics in self.couplings:
             # for each physics in self.couplings, loop over the pins and add contribution to dx
             for pin in physics.pins:
-                send = self.map[pin[0]][physics.feature_name]
-                receive = self.map[pin[1]][physics.feature_name]
-                contribution = physics(features[:, [send, receive]])
-                dx[:, [send]] += contribution
+                
+                send = list(map(self.states.get, self.agents[pin[0]].state_keys))
+                receive = list(map(self.states.get, self.agents[pin[1]].state_keys))
+                interaction_idx = list(map(self.states.get, physics.in_keys))
+
+                contribution = physics(features[:,interaction_idx]) # -> x[:,[1]] - x[:,[0]]
+                dx[:,receive] += contribution
                 if physics.symmetric:
-                    dx[:, [receive]] -= contribution
-        return dx   
+                    dx[:,send] -= contribution
+        return dx 
+
+class GeneralNetworkedAE(ODESystem):
+    """General Networked Algebraic Equation class. This is an extension of the ODESystem class
+    for handling update of algebraic states for differential-algebraic equations. Intended to 
+    be used in conjunction with GeneralNetworkedODE class.   
+    """
+    
+    def __init__(self, states = None,
+                agents = None,
+                insize = None,
+                outsize = None):
+        """Constructor method.
+        :param states: (dict) dictionary of state and index pairs, defaults to None
+        :param agents: (list(Agents)) list of agents for the networked system, defaults to None
+        :param insize: (int) in state dimension, defaults to None
+        :param outsize: (int) out state dimension, defaults to None
+        """
+        super().__init__(insize=insize, outsize=outsize)
+        self.states = states
+        self.agents = nn.ModuleList(agents)
+        self.insize = insize
+        self.outsize = outsize
+       
+    def ode_equations(self, x, *args):
+        """Forward pass of the method. For evolution of algebraic states, we call 
+        self.algebraic_equations(x) here.
+
+        :param x: (torch.Tensor) input tensor of size (batches,states)
+        :return: (torch.Tensor) output tensor of size (batches,states)
+        """
+
+        return self.algebraic_equations(x, *args)
+
+    def algebraic_equations(self, x, *args):
+        """Update of algebraic state variables according to agent-based algebra solvers or surrogates thereof.
+    
+        :param x: (torch.Tensor) input tensor of size (batches,states)
+        :return: (torch.Tensor) output tensor of size (batches,states)
+        """
+
+        features = torch.cat([x, *args], dim=-1)
+        dx = torch.clone(features)
+
+        for agent in self.agents:
+            # change the states at these indices based on the algebra solvers contained in the agents, if any
+            dx[:,list(map(self.states.get, agent.state_keys))] = agent(features[:,list(map(self.states.get, agent.in_keys))], [], mode="dae") 
+
+        return dx[:,:self.outsize]
 
 
 class TwoTankParam(ODESystem):
diff --git a/src/neuromancer/dynamics/physics.py b/src/neuromancer/dynamics/physics.py
index 9797fe82..b48498d2 100644
--- a/src/neuromancer/dynamics/physics.py
+++ b/src/neuromancer/dynamics/physics.py
@@ -12,19 +12,259 @@ class Agent(nn.Module, ABC):
     - serve as anchor for connections (pins)
     """
 
-    def __init__(self, state_names):
+    def __init__(self, state_keys):
+        """
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict
+        """
         super().__init__()
-        self.state_names = state_names
+        self.state_keys = state_keys
+
+    @abstractmethod
+    def intrinsic(self, x, y):
+        """Calcuation of the intrinsic physics contribution from a particular agent
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent
+        """
+        pass
 
     @abstractmethod
-    def intrinsic(self, x):
+    def algebra(self, x):
+        """Algebraic update of algebraic states, if any.
+
+        :param x: (torch.Tensor) input tensor of size (batches,in_keys), these are inputs to the algebra solver for the agent's algebraic states
+        """
         pass
 
-    def forward(self,x):
-        assert len(self.state_names) == x.shape[1]
-        return self.intrinsic(x)
+    def forward(self, x, y, mode: str = "ode"):
+        """_summary_
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent
+        :param mode: mode of the forward pass, "ode" or "dae", defaults to "ode"
+        :raises ValueError: invalid mode
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        if mode == "ode":
+            return self.intrinsic(x,y)
+        elif mode == "dae":
+            return self.algebra(x)
+        else:
+            raise ValueError("No match for ode or dae.")
 
 ### Children:
+   
+class SIMOConservationNode(Agent):
+    """Single Input, Multiple Output Conservation Node. Useful for splitting mass flows, etc...
+    """
+
+    def __init__(self, state_keys = None, 
+                in_keys = None, 
+                solver = None):
+        """
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)), List of strings for input, defaults to None
+        :param solver: any callable mapping from in_keys to state_keys, e.g. nn.Module or lambda function, defaults to None
+        """
+        super().__init__(state_keys=state_keys)
+        self.solver = solver
+        self.in_keys = in_keys
+
+    def intrinsic(self, x, y):
+        """No intrinsic physics contribution from conservation node, return zeros of correct size.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent        
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return torch.zeros_like(x[:,:len(self.state_keys)])
+
+    def algebra(self, x):
+        """Algebraic update for splitting single input among multiple outputs.
+
+        :param x: (torch.Tensor) input tensor of size (batches,in_keys)
+        :return: (torch.Tensor) output tensor of size (batches,state_keys)
+        """
+
+        param = torch.abs(self.solver(x[:,1:]))
+        return torch.cat((x[:,[0]]*param,x[:,[0]]*(1.0 - param)),-1)
+        #return x[:,[0]]*self.solver(x[:,1:])
+
+class SIMOBBConservationNode(Agent):
+    """Single Input, Multiple Output Conservation Node. Useful for splitting mass flows, etc...
+    """
+
+    def __init__(self, state_keys = None, 
+                in_keys = None, 
+                solver = None):
+        """
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)), List of strings for input, defaults to None
+        :param solver: any callable mapping from in_keys to state_keys, e.g. nn.Module or lambda function, defaults to None
+        """
+        super().__init__(state_keys=state_keys)
+        self.solver = solver
+        self.in_keys = in_keys
+
+    def intrinsic(self, x, y):
+        """No intrinsic physics contribution from conservation node, return zeros of correct size.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent        
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return torch.zeros_like(x[:,:len(self.state_keys)])
+
+    def algebra(self, x):
+        """Algebraic update for splitting single input among multiple outputs.
+
+        :param x: (torch.Tensor) input tensor of size (batches,in_keys)
+        :return: (torch.Tensor) output tensor of size (batches,state_keys)
+        """
+
+        return x[:,[0]]*self.solver(x[:,1:])
+
+class SISOConservationNode(Agent):
+    """Single input, single output conservation node. Useful for sources, sinks, drains, return mass flows, etc...
+    """
+
+    def __init__(self, 
+                state_keys = None, 
+                in_keys = None, 
+                solver = None):
+        """
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)), List of strings for input, defaults to None
+        :param solver: any callable mapping from in_keys to state_keys, e.g. nn.Module or lambda function, defaults to None
+        """
+        super().__init__(state_keys=state_keys)
+        self.solver = solver
+        self.in_keys = in_keys
+
+    def intrinsic(self, x, y):
+        """No intrinsic physics contribution from conservation node, return zeros of correct size.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent        
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return torch.zeros_like(x[:,:len(self.state_keys)])
+
+    def algebra(self, x):
+        """Algebraic update according to self.solver.
+
+        :param x: (torch.Tensor) input tensor of size (batches,in_keys)
+        :return: (torch.Tensor) output tensor of size (batches,state_keys)
+        """
+        return self.solver(x)
+
+class MIMOTank(Agent):
+    """Multiple Input, Multiple Output Conservation Node."""
+    def __init__(self, profile = lambda x: 1.0,
+                 state_keys = None,
+                 in_keys = None,
+                 scaling: float = 1.0):
+        """
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)), List of strings for input, defaults to None
+        :param solver: any callable mapping from in_keys to state_keys, e.g. nn.Module or lambda function, defaults to None
+        :param scaling: (float), optional scaling factor.
+        :param solver: a callable mapping agent states to a state-derived property (e.g. area-height relationship), nn.Module or lambda function
+        """
+        
+        super().__init__(state_keys = state_keys)
+        self.profile = profile
+        self.scaling = scaling
+        self.in_keys = in_keys
+
+    def intrinsic(self, x, y):
+        """Time rate of change of amount of substance in fixed volume over time, equal to sum(mass flows)/area(height).
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent  
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        # need to sum all of the inlet and outlet contributions and then scale by the 'capacitance' of the agent
+        #return torch.sum(y,1,keepdim=True)/self.profile(x)
+        return self.scaling*y/self.profile(x)
+    
+    def algebra(self, x):
+        """No algebraic update for agent's states.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return x[:,:len(self.state_keys)]
+
+class BatchReactor(Agent):
+    """Custom agent for an adiabatic batch reactor with exothermic chemical reactions."""
+
+    def __init__(self, state_keys = None,
+                 in_keys = None,
+                 C = nn.Parameter(torch.tensor(1.0)),
+                 kinetics = None):
+        """
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)), List of strings for input, defaults to None
+        :param solver: any callable mapping from in_keys to state_keys, e.g. nn.Module or lambda function, defaults to None
+        :param C: (nn.Parameter), thermal capacitance of the reactor.
+        :param kinetics: (callable) surrogate model for chemical kinetics
+        """
+        
+        super().__init__(state_keys = state_keys)
+        self.in_keys = in_keys
+        self.C = C
+        self.kinetics = kinetics
+
+    def intrinsic(self,x,y):
+        """Time rate of change of temperature of reactor obeys a first-law energy balance.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent  
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return (1.0/self.C)*(1-(x[:,[3]]/(x[:,[1]]+x[:,[2]]+x[:,[3]])))*self.kinetics(x[:,[0]])
+    
+    def algebra(self, x):
+        """No algebraic update for agent's states.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return x[:,:len(self.state_keys)]
+    
+class Reactions(Agent):
+    """Custom agent for surrogate modeling of chemical reaction network. """
+
+    def __init__(self, state_keys = None,
+                 in_keys = None,
+                 solver = None):
+        """
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)), List of strings for input, defaults to None
+        :param solver: any callable mapping from in_keys to state_keys, e.g. nn.Module or lambda function, defaults to None
+        """
+        super().__init__(state_keys=state_keys)
+        self.in_keys = in_keys
+        self.solver = solver
+
+    def intrinsic(self, x, y):
+        """No intrinsic physics contribution from conservation node, return zeros of correct size.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent        
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return torch.zeros_like(x[:,:len(self.state_keys)])
+
+    def algebra(self, x):
+        """Algebraic update according to self.solver.
+
+        :param x: (torch.Tensor) input tensor of size (batches,in_keys)
+        :return: (torch.Tensor) output tensor of size (batches,state_keys)
+        """
+        return self.solver(x)
+    
 
 class RCNode(Agent):
     """
@@ -32,36 +272,71 @@ class RCNode(Agent):
     according to the capacitance of the agent. Examples include lumped volumes, rooms, etc.
     """
 
-    def __init__(self, C = nn.Parameter(torch.tensor([1.0])), 
-                state_names = ["T"],
+    def __init__(self, state_keys = None,
+                in_keys = None,    
+                C = nn.Parameter(torch.tensor([1.0])), 
                 scaling = 1.0):
         """
-        :param C: capacitance
-        :param state_names: List of state names. Length should be same as dimension of state.
-        :param scaling: scale factor. Useful for expeceted multi-scale physics.
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)) List of strings for input, defaults to None
+        :param scaling: (float) scale factor. Useful for expeceted multi-scale physicsm defaults to 1.0
+        :param C: (nn.Parameter) learnable capacitance, defaults to a value of 1.0
         """
 
-        super().__init__(state_names=state_names)
+        super().__init__(state_keys=state_keys)
+        self.in_keys = in_keys
         self.C = C
         self.scaling = scaling
 
-    def intrinsic(self, x):
-        return torch.max(torch.tensor(1e-6),self.C)*self.scaling*x
+    def intrinsic(self, x, y):
+        """Time rate of change of temperature of reactor obeys a first-law energy balance.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent  
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return torch.max(torch.tensor(1e-6),self.C)*self.scaling*y
+    
+    def algebra(self, x):
+        """No algebraic update for agent's states.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return x[:,:len(self.state_keys)]
 
 class SourceSink(Agent):
     """
     Generic Source / Sink agent. Useful for 'dummy' agents to which one can attach external signals.
     """
 
-
-    def __init__(self, state_names = ["T"]):
+    def __init__(self, state_keys = None, 
+                in_keys = None):        
         """
-        :param state_names: List of state names. Length should be same as dimension of state.
+        :param state_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
+        :param in_keys: (list(Str)), List of strings for input, defaults to None
+        :param solver: any callable mapping from in_keys to state_keys, e.g. nn.Module or lambda function, defaults to None
         """
-        super().__init__(state_names = state_names)
+        super().__init__(state_keys=state_keys)
+        self.in_keys = in_keys
+
+    def intrinsic(self, x, y):
+        """No intrinsic physics contribution from conservation node, return zeros of correct size.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :param y: (torch.Tensor) input tensor of size (batches,agent states), these are the accumulated interactions at the agent        
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """        
+        return torch.zeros_like(x[:,:len(self.state_keys)])
+    
+    def algebra(self, x):
+        """No algebraic update for agent's states.
+
+        :param x: (torch.Tensor) input tensor of size (batches,agent states), these are the states of the agent
+        :return: (torch.Tensor) output tensor of size (batches,agent states)
+        """
+        return x[:,:len(self.state_keys)]
 
-    def intrinsic(self, x):
-        return torch.zeros_like(x)
 
 ####################### COUPLING DEFINITIONS AND SPECIFICATIONS ####################
 
@@ -72,25 +347,60 @@ class Interaction(nn.Module, ABC):
     - interactions can be one-sided or symmetric (influence both agents)
     """
 
-    def __init__(self, feature_name, pins, symmetric):
+    def __init__(self, in_keys,
+                pins: list[list[int]], 
+                symmetric: bool = False):
         """
-        :param feature_name: (str) Specification of correct state for interaction physics
+        :param in_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to None
         :param pins: list of lists of pairwise connections between agents (e.g. pins=[[0,1],[0,2]])
-        :param symmetric: one-way ot two-way interaction
+        :param symmetric: one-way or two-way interaction, default False
         """
         super().__init__()
         self.symmetric = symmetric
-        self.feature_name = feature_name
+        self.in_keys = in_keys
         self.pins = pins
 
     @abstractmethod
     def interact(self, x):
+        """Calculation of an interaction on an edge in a graph.
+
+        :param x: (torch.Tensor) input tensor of shape (batches,in keys), these are the selected input states opon which the interaction operates
+        """
         pass
 
-    def forward(self,x):
+    def forward(self, x):
+        """forward pass of the interaction.
+
+        :param x: (torch.Tensor) input tensor of shape (batches,in keys), these are the selected input states opon which the interaction operates
+        :return: (torch.Tensor)
+        """
         return self.interact(x)    
 
-### Children:
+### Children: 
+class Pipe(Interaction):
+    """
+    Imposition of a source term as an interaction.
+    """
+    def __init__(self,
+                in_keys = [], 
+                pins: list[list[int]] = [],
+                symmetric: bool = True):
+        """
+        :param in_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to []]
+        :param pins: list of lists of pairwise connections between agents (e.g. pins=[[0,1],[0,2]]), defaults to []
+        :param symmetric: one-way or two-way interaction, defaults to True
+        """
+        super().__init__(in_keys = in_keys, 
+                        pins=pins, 
+                        symmetric=symmetric)
+
+    def interact(self, x):
+        """Observation function. Return observed value from specified agent.
+
+        :param x: (torch.Tensor) input tensor of shape (batches,in keys), these are the selected input states opon which the interaction operates
+        :return: (torch.Tensor)
+        """
+        return x[:,[0]]
 
 class DeltaTemp(Interaction):
     """
@@ -98,21 +408,29 @@ class DeltaTemp(Interaction):
     """
 
     def __init__(self,
+                in_keys = [],
+                pins: list[list[int]] = [],
                 R = nn.Parameter(torch.tensor(1.0)), 
-                feature_name = "T", 
-                symmetric = False,
-                pins = []):
+                symmetric = False):
         """
-        :param R: resistivity for connection
-        :param feature_name: (str) Specification of correct state for interaction physics
-        :param pins: list of lists of pairwise connections between agents (e.g. pins=[[0,1],[0,2]])
-        :param symmetric: one-way ot two-way interaction
+        :param in_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to []]
+        :param pins: list of lists of pairwise connections between agents (e.g. pins=[[0,1],[0,2]]), defaults to []
+        :param R: (nn.Parameter) 1/Resistance, learnable, defaults to 1.0
+        :param symmetric: one-way or two-way interaction, defaults to True
         """
 
-        super().__init__(feature_name=feature_name, pins=pins, symmetric=symmetric)
+        super().__init__(in_keys = in_keys, 
+                        pins=pins, 
+                        symmetric=symmetric)
         self.R = R
 
     def interact(self, x):
+        """calculation of temperature difference (or, abstractly, any state difference) between agents.
+        Scales the temperature difference by (R = 1/Resistance).
+
+        :param x: (torch.Tensor) input tensor of shape (batches,in keys), these are the selected input states opon which the interaction operates
+        :return: (torch.Tensor)
+        """
         return torch.max(torch.tensor(1e-2),self.R)*(x[:,[1]] - x[:,[0]])
 
 class DeltaTempSwitch(Interaction):
@@ -121,21 +439,30 @@ class DeltaTempSwitch(Interaction):
     depending on agent values (zero or nonzero).
     """
     def __init__(self,
-                R = nn.Parameter(torch.tensor([1.0])), 
-                feature_name = "T", 
-                symmetric = False,
-                pins = []):
+                in_keys = [],
+                pins: list[list[int]] = [],
+                R = nn.Parameter(torch.tensor(1.0)), 
+                symmetric = False):
         """
-        :param R: resistivity for connection
-        :param feature_name: (str) Specification of correct state for interaction physics
-        :param pins: list of lists of pairwise connections between agents (e.g. pins=[[0,1],[0,2]])
-        :param symmetric: one-way ot two-way interaction
+        :param in_keys: (list(Str)) List of strings corresponding to keys in the ODE system's state dict, defaults to []]
+        :param pins: list of lists of pairwise connections between agents (e.g. pins=[[0,1],[0,2]]), defaults to []
+        :param R: (nn.Parameter) 1/Resistance, learnable, defaults to 1.0
+        :param symmetric: one-way or two-way interaction, defaults to True
         """
 
-        super().__init__(feature_name=feature_name, pins=pins, symmetric=symmetric)
+        super().__init__(in_keys = in_keys, 
+                        pins=pins, 
+                        symmetric=symmetric)
         self.R = R
 
     def interact(self, x):
+        """calculation of temperature difference (or, abstractly, any state difference) between agents.
+        Scales the temperature difference by (R = 1/Resistance).
+        Returns a minimum value (default is 1e-2) if paired agent's state value is zero.
+
+        :param x: (torch.Tensor) input tensor of shape (batches,in keys), these are the selected input states opon which the interaction operates
+        :return: (torch.Tensor)
+        """
         return torch.max(torch.tensor(1e-2),self.R)*(x[:,[1]] - x[:,[0]])*(x[:,[1]]>=0.0)
 
 class HVACConnection(Interaction):
@@ -143,40 +470,35 @@ class HVACConnection(Interaction):
     Imposition of a source term as an interaction.
     """
     def __init__(self,
-                feature_name = "T",
-                symmetric = False, 
-                pins = []):
+                in_keys = [],
+                pins: list[list[int]] = [],
+                symmetric = False):
         """
         :param feature_name: (str) Specification of correct state for interaction physics
         :param pins: list of lists of pairwise connections between agents (e.g. pins=[[0,1],[0,2]])
         :param symmetric: one-way ot two-way interaction
         """
-        super().__init__(feature_name=feature_name, pins=pins, symmetric=symmetric)
+        super().__init__(in_keys = in_keys, 
+                        pins=pins, 
+                        symmetric=symmetric)
 
     def interact(self, x):
-        return x[:,[1]] 
-
-################################# HELPER FUNCTIONS #################################
+        """Observation function. Return observed value from specified agent.
 
-def map_from_agents(intrinsic_list):
-    """
-    Quick helper function to construct state mappings:
-    """
+        :param x: (torch.Tensor) input tensor of shape (batches,in keys), these are the selected input states opon which the interaction operates
+        :return: (torch.Tensor)
+        """
+        return x[:,[1]] 
     
-    agent_maps = []
-    count = 0
-    for agent_physics in intrinsic_list:
-        node_states = [(s,i+count) for i,s in enumerate(agent_physics.state_names)]
-        count += len(node_states)
-        agent_maps.append(OrderedDict(node_states))
-
-    return agent_maps
-
-### Aggregate all in dicts:
-
-agents = {'RCNode': RCNode,
+agents = {'SIMOConservationNode': SIMOConservationNode,
+          'SISOConservationNode': SISOConservationNode,
+          'MIMOTank': MIMOTank,
+          'BatchReactor': BatchReactor,
+          'Reactions': Reactions,
+          'RCNode': RCNode,
           'SourceSink': SourceSink}
 
-couplings = {'DeltaTemp': DeltaTemp,
+couplings = {'Pipe': Pipe,
+             'DeltaTemp': DeltaTemp,
              'DeltaTempSwitch': DeltaTempSwitch,
              'HVACConnection': HVACConnection}
\ No newline at end of file
diff --git a/src/neuromancer/modules/blocks.py b/src/neuromancer/modules/blocks.py
index 081e09bc..d2cc5972 100644
--- a/src/neuromancer/modules/blocks.py
+++ b/src/neuromancer/modules/blocks.py
@@ -37,6 +37,16 @@ def forward(self, *inputs):
             x = inputs[0]
         return self.block_eval(x)
 
+class Drain(nn.Module, ABC):
+    """
+    Canonical abstract class of the block function approximator
+    """
+    def __init__(self):
+        super().__init__()
+        self.coeff = torch.nn.Parameter(torch.tensor(1.0), requires_grad=True)
+
+    def forward(self, x):
+        return torch.abs(self.coeff)*torch.sqrt(torch.abs(x))
 
 class Linear(Block):
     """