On generalization error estimates of physics informed neural networks for approximating dispersive PDEs
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Date
2021-04
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Report
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Abstract
Physics informed neural networks (PINNs) have recently been widely used for robust and accurate approximation of PDEs. We provide rigorous upper bounds on the generalization error of PINNs approximating solutions of the forward problem for several dispersive PDEs.
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published
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2021-13
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Publisher
Seminar for Applied Mathematics, ETH Zurich
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Subject
Deep learning; Physics informed neural networks (PINNs); Dispersive PDEs
Organisational unit
03851 - Mishra, Siddhartha / Mishra, Siddhartha