Curriculum Learning for Mesh-based simulations

Paul Garnier; Vincent Lannelongue; Elie Hachem

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arXiv · 2509.13138

Curriculum Learning for Mesh-based simulations

Paul Garnier·Vincent Lannelongue·Elie Hachem

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Abstract

Graph neural networks (GNNs) have emerged as powerful surrogates for mesh-based computational fluid dynamics (CFD), but training them on high-resolution unstructured meshes with hundreds of thousands of nodes remains prohibitively expensive. We study a \emph{coarse-to-fine curriculum} that accelerates convergence by first training on very coarse meshes and then progressively introducing medium and high resolutions (up to $3\times10^5$ nodes). Unlike multiscale GNN architectures, the model itself is unchanged; only the fidelity of the training data varies over time. We achieve comparable generalization accuracy while reducing total wall-clock time by up to 50\%. Furthermore, on datasets where our model lacks the capacity to learn the underlying physics, using curriculum learning enables it to break through plateaus.

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Paul Garnier, Vincent Lannelongue, Elie Hachem. 2025-09-16. Curriculum Learning for Mesh-based simulations. https://arxiv.org/abs/2509.13138

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