From non-convex self-concordant regularization to scalable quasi-Newton training of PINNs
Read the original at arxiv.org→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...
Original headline: "From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs"
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- Aug 6, 04:00 UTC arXiv cs.LG lead source From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs