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arXiv Machine Learning By Hyeonsu Lee, Jihoon Jeong

Domain-informed Adaptive Sampling for Generalizable PINNs in Metal Additive Manufacturing via Conditional Flow Matching

Read the original on arXiv Machine Learning →

The paper introduces a domain-informed adaptive sampling method for physics-informed neural networks (PINNs) applied to metal additive manufacturing. By combining a conditional Flow Matching model with a domain-informed base distribution, the approach generates adaptive collocation points that better capture process-dependent heat dissipation regions. Experiments on numerical benchmarks show a 62.1% reduction in relative L2 error compared to state‑of‑the‑art PINN baselines.

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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