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

Excerpt

arXiv:2608.04206v2 Announce Type: replace 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. Regularized quasi-Newton methods provide established mechanisms for stabilizing secant models, while self-concordant methods provide local-metric rules for curvature-dependent step selectio