Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws

• URL: http://arxiv.org/abs/2401.01783v4
• Date: Mon, 13 May 2024 15:53:38 GMT
• Title: Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
• Authors: Taeyoung Kim, Myungjoo Kang,
• Abstract summary: neural network-based methods, such as physics-informed neural networks (PINNs) and neural operators, exhibit deficiencies in robustness and generalization. In this study, we focus on hyperbolic conservation laws by replacing traditional numerical flux with neural operators. Our approach combines the strengths of both traditional numerical schemes and FNOs, outperforming standard FNO methods in several respects.
• Score: 7.438389089520601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditionally, classical numerical schemes have been employed to solve partial differential equations (PDEs) using computational methods. Recently, neural network-based methods have emerged. Despite these advancements, neural network-based methods, such as physics-informed neural networks (PINNs) and neural operators, exhibit deficiencies in robustness and generalization. To address these issues, numerous studies have integrated classical numerical frameworks with machine learning techniques, incorporating neural networks into parts of traditional numerical methods. In this study, we focus on hyperbolic conservation laws by replacing traditional numerical fluxes with neural operators. To this end, we developed loss functions inspired by established numerical schemes related to conservation laws and approximated numerical fluxes using Fourier neural operators (FNOs). Our experiments demonstrated that our approach combines the strengths of both traditional numerical schemes and FNOs, outperforming standard FNO methods in several respects. For instance, we demonstrate that our method is robust, has resolution invariance, and is feasible as a data-driven method. In particular, our method can make continuous predictions over time and exhibits superior generalization capabilities with out-of-distribution (OOD) samples, which are challenges that existing neural operator methods encounter.

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