Weak physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
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Date
2022-07Type
- Report
ETH Bibliography
yes
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Abstract
Physics informed neural networks (PINNs) require regularity of solutions of the underlying PDE to guarantee accurate approximation. Consequently, they may fail at approximating discontinuous solutions of PDEs such as nonlinear hyperbolic equations. To ameliorate this, we propose a novel variant of PINNs, termed as weak PINNs (wPINNs) for accurate approximation of entropy solutions of scalar conservation laws. wPINNs are based on approximating the solution of a min-max optimization problem for a residual, defined in terms of Kruzkhov entropies, to determine parameters for the neural networks approximating the entropy solution as well as test functions. We prove rigorous bounds on the error incurred by wPINNs and illustrate their performance through numerical experiments to demonstrate that wPINNs can approximate entropy solutions accurately Show more
Publication status
publishedExternal links
Journal / series
SAM Research ReportVolume
Publisher
Seminar for Applied Mathematics, ETH ZurichOrganisational unit
03851 - Mishra, Siddhartha / Mishra, Siddhartha
Funding
770880 - Computation and analysis of statistical solutions of fluid flow (EC)
Related publications and datasets
Is previous version of: http://hdl.handle.net/20.500.11850/665733
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ETH Bibliography
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