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arXiv Machine Learning By Nathanael Tepakbong, Hanyu Hu, Chengyu Liu, Xiang Zhou

Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds

Read the original on arXiv Machine Learning →

arXiv:2606. 00643v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recently linked to severely ill-conditioned loss landscapes inherited from the underlying differential operator.

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

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