Nonlinear Reconstruction for Operator Learning of PDEs with
Discontinuities
- URL: http://arxiv.org/abs/2210.01074v1
- Date: Mon, 3 Oct 2022 16:47:56 GMT
- Title: Nonlinear Reconstruction for Operator Learning of PDEs with
Discontinuities
- Authors: Samuel Lanthaler and Roberto Molinaro and Patrik Hadorn and Siddhartha
Mishra
- Abstract summary: A large class of hyperbolic and advection-dominated PDEs can have solutions with discontinuities.
We rigorously prove, in terms of lower approximation bounds, that methods which entail a linear reconstruction step fail to efficiently approximate the solution operator of such PDEs.
We show that certain methods employing a non-linear reconstruction mechanism can overcome these fundamental lower bounds and approximate the underlying operator efficiently.
- Score: 5.735035463793008
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A large class of hyperbolic and advection-dominated PDEs can have solutions
with discontinuities. This paper investigates, both theoretically and
empirically, the operator learning of PDEs with discontinuous solutions. We
rigorously prove, in terms of lower approximation bounds, that methods which
entail a linear reconstruction step (e.g. DeepONet or PCA-Net) fail to
efficiently approximate the solution operator of such PDEs. In contrast, we
show that certain methods employing a non-linear reconstruction mechanism can
overcome these fundamental lower bounds and approximate the underlying operator
efficiently. The latter class includes Fourier Neural Operators and a novel
extension of DeepONet termed shift-DeepONet. Our theoretical findings are
confirmed by empirical results for advection equation, inviscid Burgers'
equation and compressible Euler equations of aerodynamics.
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