Using a Tensorflow-based implementation of Neural ODEs (NODE) to develop non-intrusive reduced order models for CFD problems. Numerical comparisons are made with non-intrusive reduced order models (NIROM) that use Dynamic Mode Decomposition (DMD) as well as a combination of linear dimension reduction using Proper Orthogonal Decomposition (POD) and latent-space evolution using Radial Basis Function (RBF) interpolation.
For details please refer to
S. Dutta, P. Rivera-casillas, and M. W. Farthing, “Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics,” in Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, 2021. Link
- Python 3.x
- Tensorflow 2.x, 1.15.x
- tfdiffeq
- NODE scripts, available inside the notebooks directory, can be invoked with various user-specified configuration options to test different NN models
- DMD and PODRBF notebooks are also available inside the notebooks directory.
- High-fidelity snapshot data files are available at HFM data. These should be placed in the <node_nirom/data/> directory.
- Some pre-trained ROM model files are available at NIROM models. The DMD and PODRBF trained models should be placed in the <node_nirom/data/> directory, and the NODE models should be placed inside the corresponding subdirectory of <node_nirom/best_models>.
- Sourav Dutta - Sourav.Dutta@erdc.dren.mil - ERDC-CHL
- Matthew Farthing - Matthew.W.Farthing@erdc.dren.mil - ERDC-CHL
- Peter Rivera-Casillas - Peter.G.Rivera-Casillas@erdc.dren.mil - ERDC-CHL
This project is licensed under the MIT License - see the LICENSE file for details
If you found this library useful in your research, please consider citing
@inproceedings{dutta2021aaai,
title={Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics},
author={Dutta, Sourav and Rivera-Casillas, Peter and Farthing, Matthew W.},
booktitle={Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences},
url={https://sites.google.com/view/aaai-mlps/proceedings?authuser=0},
year={2021},
publisher={CEUR-WS},
address={Stanford, CA, USA, March 22nd to 24th, 2021},
}
- Thank you to ERDC-HPC facilities for support with valuable computational infrastructure
- Thank you to ORISE for support with appointment to the Postgraduate Research Participation Program.
Inspiration, code snippets, etc.