An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have emerged as a promising route to solve partial differential equations, yet they have struggled to reach the precision of classical solvers. The obstacle is increasingly understood to be one of optimisation, owing to the severely ill-conditioned loss landscape. We present $\textbf{DSGNAR}$: Doubly-Sketched Gauss-Newton with Adaptive Ratio, a scalable second-order optimisation framework that confronts this ill-conditioning and, in doing so, obtains unprecedented accuracy and speed. $\textbf{DSGNAR}$ couples a doubly-sketched Gauss-Newton model with a
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- Linked via arxiv authorJoseph Webb →
An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
- Linked via arxiv authorSadok Jerad →
An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
- Linked via arxiv authorCoralia Cartis →
An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks