Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

We propose a conservative physics-informed neural network (cPINN) on discrete domains for nonlinear conservation laws. Here, the term discrete domain represents the discrete sub-domains obtained after division of the computational domain, where PINN is applied and the conservation property of cPINN...

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Bibliographic Details
Published in:	Computer methods in applied mechanics and engineering Vol. 365; no. C; p. 113028
Main Authors:	Jagtap, Ameya D., Kharazmi, Ehsan, Karniadakis, George Em
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 15.06.2020 Elsevier BV Elsevier
Subjects:	Activation Algorithms Artificial neural networks Compressibility Computational fluid dynamics Conservation laws cPINN Domain decomposition Euler-Lagrange equation Fluxes Inverse problems Machine learning Mortar PINN Neural networks Optimization Parallel processing Training Conservation laws Inverse problems cPINN Mortar PINN Domain decomposition Machine learning
ISSN:	0045-7825, 1879-2138
Online Access:	Get full text
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