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arXiv Machine Learning By Siddharth Verma, Jaewan Park, Jay Phil Yoo, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam

Flow Mesh: A Velocity-Regularized Flow Matching Framework for Stable Distributional Modeling of Physical Fields on Unstructured Meshes

Read the original on arXiv Machine Learning →

FlowMesh is a generative framework that produces full-field thermal‑fluid realizations directly on fixed unstructured meshes by combining permutation‑invariant mesh encoding, deterministic latent flow matching, and an l1 penalty on transport velocity. It achieves high probability‑density‑function coverage (91.7%–97.0%) and significant reductions in temperature‑field Kullback–Leibler divergence compared to latent diffusion models, while generating each realization in about 1.2 s—over 500‑fold faster than traditional computational fluid dynamics. The method enables large‑scale, data‑driven characterization of thermal‑fluid states on complex geometries without the need for grid interpolation or stochastic denoising chains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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