Automated discovery of finite volume schemes using Graph Neural Networks

• URL: http://arxiv.org/abs/2508.19052v1
• Date: Tue, 26 Aug 2025 14:08:46 GMT
• Title: Automated discovery of finite volume schemes using Graph Neural Networks
• Authors: Paul Garnier, Jonathan Viquerat, Elie Hachem,
• Abstract summary: We establish that Graph Neural Networks (GNNs) can serve purposes beyond their traditional role.<n>We show that a GNN trained on a dataset consisting solely of two-node graphs can extrapolate a first-order Finite Volume scheme.<n>Using symbolic regression, we show that the network effectively rediscovers the exact analytical formulation of the standard first-order FV scheme.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNNs) have deeply modified the landscape of numerical simulations by demonstrating strong capabilities in approximating solutions of physical systems. However, their ability to extrapolate beyond their training domain (\textit{e.g.} larger or structurally different graphs) remains uncertain. In this work, we establish that GNNs can serve purposes beyond their traditional role, and be exploited to generate numerical schemes, in conjunction with symbolic regression. First, we show numerically and theoretically that a GNN trained on a dataset consisting solely of two-node graphs can extrapolate a first-order Finite Volume (FV) scheme for the heat equation on out-of-distribution, unstructured meshes. Specifically, if a GNN achieves a loss $\varepsilon$ on such a dataset, it implements the FV scheme with an error of $\mathcal{O}(\varepsilon)$. Using symbolic regression, we show that the network effectively rediscovers the exact analytical formulation of the standard first-order FV scheme. We then extend this approach to an unsupervised context: the GNN recovers the first-order FV scheme using only a residual loss similar to Physics-Informed Neural Networks (PINNs) with no access to ground-truth data. Finally, we push the methodology further by considering higher-order schemes: we train (i) a 2-hop and (ii) a 2-layers GNN using the same PINN loss, that autonomously discover (i) a second-order correction term to the initial scheme using a 2-hop stencil, and (ii) the classic second-order midpoint scheme. These findings follows a recent paradigm in scientific computing: GNNs are not only strong approximators, but can be active contributors to the development of novel numerical methods.

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