Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

•Deep auto-regressive dense encoder-decoder surrogate for predicting transient PDEs.•Physics-constrained learning enables the model to learn dynamics without training data.•A Bayesian framework is proposed for interpretable uncertainty quantification of the models' predictions at each time-step...

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Veröffentlicht in:	Journal of computational physics Jg. 403; S. 109056
Hauptverfasser:	Geneva, Nicholas, Zabaras, Nicholas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 15.02.2020 Elsevier Science Ltd
Schlagworte:	Artificial neural networks Auto-regressive model Burgers equation Coders Computational physics Convolutional encoder-decoder Deep learning Deep neural networks Dynamic partial differential equations Encoders-Decoders Machine learning Mathematical models Model testing Nonlinear systems Numerical analysis Numerical methods Partial differential equations Physics-informed machine learning Regression analysis Solvers Training Uncertainty Uncertainty quantification Physics-informed machine learning Uncertainty quantification Auto-regressive model Deep neural networks Dynamic partial differential equations Convolutional encoder-decoder
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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