arXiv Machine Learning August 6, 2026 By Chenhao Si, Kang An, Shiqian Ma, Ming Yan

From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs

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arXiv:2608. 04206v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) often require high-accuracy quasi-Newton refinement to obtain reliable partial differential equation solutions, but their residual objectives can exhibit indefinite, nearly singular, and poorly scaled local curvature.

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

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