Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks [JCP][ArXiv]
Nicholas Geneva, Nicholas Zabaras
| Highlights | Bayesian 2D Coupled Burgers' Prediction |
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In this work, we propose a novel auto-regressive dense encoder-decoder convolutional neural network to solve and model transient systems with non-linear dynamics at a computational cost that is potentially magnitudes lower than standard numerical solvers. This model includes a Bayesian framework that allows for uncertainty quantification of the predicted quantities of interest at each time-step. We rigorously test this model on several non-linear transient partial differential equation systems including the turbulence of the Kuramoto-Sivashinsky equation, multi-shock formation and interaction with 1D Burgers’ equation and 2D wave dynamics with coupled Burgers’ equations.
Each of the PDE systems used in the paper is designated its own folder where more information can be found regarding model training, testing and figure replication.
- 1D-KS-SWAG: The 1D Kuramoto-Sivashinsky system.
- 1D-Burger-SWAG: The 1D viscous Burgers' system.
- 2D-Burgers-SWAG: The 2D coupled Burgers' system.
| Deep Turbulence Generation |
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- Python 3.6.5
- PyTorch 1.1.0
- Fenics 2019.1.0
- Matplotlib 3.1.1
Plus additional standard packages such as Numpy and Scipy
Find this useful or like this work? Cite us with:
@article{geneva2019modeling,
title = {Modeling the dynamics of {PDE} systems with physics-constrained deep auto-regressive networks},
journal = {Journal of Computational Physics},
pages = {109056},
year = {2019},
issn = {0021-9991},
doi = {10.1016/j.jcp.2019.109056},
url = {http://www.sciencedirect.com/science/article/pii/S0021999119307612},
author = {Nicholas Geneva and Nicholas Zabaras}
}