English(EN) Linearized PINN with pretrained nonlinear layers

新的lPINN方法可大幅缩短求解微分方程的时间

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种线性化物理信息神经网络（lPINN），该方法显著加快了求解微分方程的过程。该方法包括一个离线阶段，从中学习连续神经网络基函数，这些基函数来自数值解和物理残差。对于新的问题实例，通过最小化控制方程残差在线计算解，使用这些固定的基函数，与传统的PINN相比，推理时间减少了一到三个数量级。lPINN方法还展示了在不重新训练的情况下在更精细的网格上评估学习表示的能力，同时保持准确性。 AI

影响通过实现对复杂微分方程更快、更准确的求解，加速科学研究。

排序理由详细介绍求解微分方程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Wenhao Chen, Alexandre M. Tartakovsky · 2026-09-15 04:00

预训练非线性层的线性化PINN

arXiv:2609.14926v1 Announce Type: cross Abstract: We propose a linearized Physics-Informed Neural Network (lPINN), a reduced-order neural basis method for forward and inverse differential equations. In an offline stage, lPINN learns operator-compatible continuous neural basis fun…

本文如何被排名

报道来源 [1]

预训练非线性层的线性化PINN

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