English(EN) SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning

新的SS-ESOAP方法增强了物理信息神经网络的训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了SS-ESOAP，一种旨在改进物理信息神经网络（PINNs）训练的新型预处理方法。该方法解决了ill-conditioned objectives的挑战，这些目标经常阻碍PINNs的高精度训练。SS-ESOAP结合了自适应基更新、标量割线能量校正和方差状态下缩放，在Burgers和Boussinesq方程等多个基于物理的基准测试中表现优于SOAP和Adam等现有方法。 AI

影响该方法为在stiff、物理信息训练场景中实现高精度提供了一种可扩展的方法。

排序理由该集群包含一篇详细介绍物理信息学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Guangyuan Wang, Mads Toftrup, Sebastian Loeschcke, Yixuan Wang, Anima Anandkumar · 2026-09-01 04:00

SS-ESOAP：物理信息学习的自尺度自适应预处理

arXiv:2608.29448v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) often face ill-conditioned objectives that limit high-accuracy training. Dense quasi-Newton methods improve local conditioning but require expensive optimizer state, while Kronecker-factore…

本文如何被排名

报道来源 [1]

SS-ESOAP：物理信息学习的自尺度自适应预处理

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