arXiv Machine Learning July 14, 2026 By Zhangyi Lian, Xinda Dong, Wenxuan Huo, Weifeng Huang, Gang Zhu, Qiang He

Multi-dimensional training-priority weighting based on physical information propagation paths: a unified residual-weighting framework for physics-informed neural networks

Read the original on arXiv Machine Learning →

arXiv:2607. 11094v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations (PDEs); however, their synchronous optimization treats residuals of different regions and constraints equally, which is inconsistent with the progressive "from source to response" physical information propagation path, degrading training stability and accuracy.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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On the training of physics-informed neural operators for solving parametric partial differential equations

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