Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra

TL;DR: Operator learning based on non-linear reconstruction (FNOs, shift-DeepONets) outperform methods based on linear reconstruction (DeepONets, PCA-Net) for PDEs with discontinuities.

Abstract: Discontinuous solutions arise in a large class of hyperbolic and advection-dominated PDEs. 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. DeepONets or PCA-Nets) 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 DeepONets termed shift-DeepONets. Our theoretical findings are confirmed by empirical results for advection equations, inviscid Burgers’ equation and the compressible Euler equations of gas dynamics.

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