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arXiv · 2206.14687

Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators

Léon Migus·Yuan Yin·Jocelyn Ahmed Mazari·Patrick Gallinari
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Abstract

Representing physical signals at different scales is among the most challenging problems in engineering. Several multi-scale modeling tools have been developed to describe physical systems governed by \emph{Partial Differential Equations} (PDEs). These tools are at the crossroad of principled physical models and numerical schema. Recently, data-driven models have been introduced to speed-up the approximation of PDE solutions compared to numerical solvers. Among these recent data-driven methods, neural integral operators are a class that learn a mapping between function spaces. These functions are discretized on graphs (meshes) which are appropriate for modeling interactions in physical phenomena. In this work, we study three multi-resolution schema with integral kernel operators that can be approximated with \emph{Message Passing Graph Neural Networks} (MPGNNs). To validate our study, we make extensive MPGNNs experiments with well-chosen metrics considering steady and unsteady PDEs.

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Léon Migus, Yuan Yin, Jocelyn Ahmed Mazari, Patrick Gallinari. 2022-06-29. Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators. https://arxiv.org/abs/2206.14687

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