Neural-Network-Based Surrogates for Reduced Order Model Dynamics (ROMNet)

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Executing RomNET:

1. In $WORKSPACE_PATH, create a ROMNET folder

2. Inside $WORKSPACE_PATH/ROMNet/, clone the ROMNet repository and rename it "romnet"

   Note: $WORKSPACE_PATH ├── ... ├── ROMNET │ └── romnet ├── app ├── database ├── ...

3. From $WORKSPACE_PATH/ROMNet/romnet/, install the code (i.e., $ python3 setup.py install)

4. From $WORKSPACE_PATH/ROMNet/romnet/app/, launch the code (i.e., $ python3 RomNet.py path-to-input-folder) (e.g. python3 RomNet.py ../input/MassSpringDamper/DeepONet/)

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Test Cases:

Please, refer to the presentations in $WORKSPACE_PATH/ROMNet/romnet/docs/test_cases for info and running instructions

List of Implemented Test Cases (Note: it is highly recommended to run the test cases in the suggested order):

- 1. Mass-Spring-Damper System

	- MSD_TestCase1:    Fully data-driven training of Vanilla DeepONet
	- MSD_TestCase2:    Fully data-driven training of POD-DeepONet
	- MSD_TestCase3:    Predictions from SVD-DeepONet
	- MSD_TestCase4:    Physics-informed training of Vanilla DeepONet
	- MSD_TestCase5:    Probabilistic Vanilla DeepONet via MC Dropout
	- MSD_TestCase6:    Probabilistic Vanilla DeepONet via Variational Inference with deterministic parameters and fixed Likelihood's SD
	- MSD_TestCase7:    Probabilistic Vanilla DeepONet via Variational Inference with deterministic parameters and calibrated Likelihood's SD
	- MSD_TestCase8:    Probabilistic Vanilla DeepONet via Variational Inference with random parameters (i.e., TFP layers) and fixed Likelihood's SD
	- MSD_TestCase9:    Probabilistic Vanilla DeepONet via Variational Inference with random parameters (i.e., TFP layers) and calibrated Likelihood's SD
	- MSD_TestCase10:  Fully data-driven training of MIONet
	

- 2. Translating Hyperbolic Function
	
	- TransTanh_TestCase1: Fully data-driven training of Vanilla DeepONet
	- TransTanh_TestCase1: Fully data-driven training of POD-DeepONet
	- TransTanh_TestCase3: Fully data-driven training of flexDeepONet
	- TransTanh_TestCase4: Physics-informed training of Vanilla DeepONet
	- TransTanh_TestCase5: Physics-informed training of flexDeepONet


- 3. 0D Isobaric Reactor 

	- 0DReact_*_TestCase1: Fully data-driven training of Vanilla DeepONet in the original (i.e., thermodynamic state variables) space
	- 0DReact_*_TestCase2: Fully data-driven training of POD-DeepONet in the original (i.e., thermodynamic state variables) space
	- 0DReact_*_TestCase3: Fully data-driven training of flexDeepONet in the original (i.e., thermodynamic state variables) space
	- 0DReact_*_TestCase4: -
	- 0DReact_*_TestCase5: Fully data-driven training of Vanilla DeepONet in the reduced-order (i.e., principal components) space
	- 0DReact_*_TestCase6: Fully data-driven training of flexDeepONet in the reduced-order (i.e., principal components) space
	- 0DReact_*_TestCase7: -
	- 0DReact_*_TestCase8: -
	- 0DReact_*_TestCase9: -
	- 0DReact_*_TestCase10: Test case for comparing flexDeepONet to flexMIONet
	- 0DReact_*_TestCase11: Frozen trunks (precomputed NNs from scenario-aggregated dimensionality reductions) DeepONet
	- 0DReact_*_TestCase12: NonLinear-DeepONet: flexDeepONet with a FNN decoder replacing the dot-product layer
	- 0DReact_*_TestCase13: SharedTrunks-DeepONet: flexDeepONet with shared trunks
	
- 4. Moving Rectangle

	- Rect_TestCase1: Fully data-driven training of vanilla DeepONet for a rotating-translating-scaling rigid body (~ rectangle)
	- Rect_TestCase2: Fully data-driven training of flexDeepONet for a rotating-translating-scaling rigid body (~ rectangle)
	
- 5. Evolving PDF
	
	- PDFEvolve_TestCase1: Evolving Multimodal PDFs binned in 50 groups 
	- PDFEvolve_TestCase2: Evolving Multimodal CDFs binned in 50 groups 

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Implemented NN Algorithms:

• Deterministic Neural Networks and DeepONets

• Probabilistic Neural Networks and DeepONets

  • Monte Carlo Dropout

  • Bayes by Backprop