Update document

README.md

@@ -21,7 +21,7 @@
 
 DeepFlame is a deep learning empowered computational fluid dynamics package for single or multiphase, laminar or turbulent, reacting flows at all speeds. It aims to provide an open-source platform to combine the individual strengths of [OpenFOAM](https://openfoam.org), [Cantera](https://cantera.org), and [PyTorch](https://pytorch.org/) libraries for deep learning assisted reacting flow simulations. It also has the scope to leverage the next-generation heterogenous supercomputing and AI acceleration infrastructures such as GPU and FPGA.
 
-The deep learning algorithms and models used in the DeepFlame tutorial examples are developed and trained independently by our collaborators team – [Intelligent Combustion](https://github.com/intelligent-algorithm-team/intelligent-combustion.git). Please refer to their website for detailed information.
+The neural network models used in the tutorial examples can be found at– [AIS Square](https://www.aissquare.com/). To run DeepFlame with DNN, download the DNN model [dfODENet](https://www.aissquare.com/models/detail?pageType=models&name=dfODENet_DNNmodel_V0.1&id=181) into the case folder you would like to run.
 
 ## Documentation
 Detailed guide for installation and tutorials is available on [our documentation website](https://deepflame.deepmodeling.com).

docs/source/qs/download_dnn_models.rst

@@ -1,18 +1,11 @@
 Download DNN Models
 ======================================
-The neural network models used in the tutorial examples are indepentently trained
-by our collaborators team – `Intelligent Combustion <https://github.com/intelligent-algorithm-team/intelligent-combustion.git>`_. 
-To run DeepFlame with DNN, first download the DeepCombustion repository into ``deepflame-dev/``: 
+The neural network models used in the tutorial examples can be found at– `AIS Square <https://www.aissquare.com/>`_. 
+To run DeepFlame with DNN, download the DNN model `dfODENet <https://www.aissquare.com/models/detail?pageType=models&name=dfODENet_DNNmodel_V0.1&id=181>`_ into the case folder you would like to run. You can either click the 'Download' on the website or:
 
 .. code-block:: bash
 
-    cd $DF_ROOT
-    git clone https://github.com/intelligent-algorithm-team/intelligent-combustion.git.git 
+    cd ``case folder``
+    wget --content-disposition ``the network's download link``
     
-Then copy the required DNN model into ``mechanisms/``, for example:
 
-.. code-block:: bash
-
-    cp -r intelligent-combustion/DeePCK/Model/HE04_Hydrogen_ESH2_GMS_sub_20221101/ mechanisms/
-    
-.. Note:: Here ``HE04_Hydrogen_ESH2_GMS_sub_20221101`` is the default DNN model for all the tutorial cases in ``$DF_ROOT/examples/``.

docs/source/qs/install.rst

@@ -43,10 +43,9 @@ Alternatively, one can `compile OpenFOAM-7 from source code <https://openfoam.or
 
     conda create -n deepflame python=3.8
     conda activate deepflame
-    conda install -c cantera libcantera-devel
+    conda install -c cantera libcantera-devel=2.6 cantera
     pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
-    conda install pybind11 
-    conda install -c conda-forge easydict
+    conda install pybind11 pkg-config
 
 .. Note:: Please go to PyTorch's official website to check your system compatability and choose the installation command line that is suitable for your platform. After installing torch, do check if torch.cuda.is_available() returns true to use GPU for DNN inference!