Curriculum Learning for Mesh-based simulations

• URL: http://arxiv.org/abs/2509.13138v1
• Date: Tue, 16 Sep 2025 14:54:11 GMT
• Title: Curriculum Learning for Mesh-based simulations
• Abstract summary: Graph neural networks (GNNs) have emerged as powerful surrogates for mesh-based computational fluid dynamics (CFD)<n>We study a emphcoarse-to-fine curriculum that accelerates convergence by first training on very coarse meshes.<n>We achieve comparable accuracy while reducing total wall-clock time by up to 50%.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by/4.0/
• 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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