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

新的自适应采样策略增强了金属增材制造中PINNs的性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了一种新的物理信息神经网络（PINNs）自适应采样策略，以提高其在金属增材制造中的泛化能力。该方法在最近的一篇arXiv论文中有所介绍，它使用条件流匹配模型来学习高残差区域的分布，然后将其与域信息自适应采样相结合，以生成更有效的配置点。实验表明，与传统的静态采样方法相比，该方法显著降低了误差，为金属增材制造过程提供了更好的热建模。 AI

影响提高了用于复杂工业模拟的AI模型的准确性和泛化能力。

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在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Hyeonsu Lee, Jihoon Jeong · 2026-10-08 04:00

面向金属增材制造中通用PINN的领域感知自适应采样方法：基于条件流匹配

arXiv:2610.09126v1 Announce Type: new Abstract: Accurate thermal modeling is essential in metal additive manufacturing (AM) for understanding the process-structure-property chain. Physics-informed neural networks (PINNs) offer effective surrogate thermal modeling by minimizing ph…

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面向金属增材制造中通用PINN的领域感知自适应采样方法：基于条件流匹配

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