Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
Physics-informed neural networks (PINNs) offer a mesh-free route to solving coupled multiphysics systems, but their accuracy degrades systematically as inter-equation coupling strengthens, and inverse-gradient-norm loss balancing alone does not reliably prevent this failure. This study explains why coupling degrades PINN training and identifies an optimizer structure that removes the dependence, replacing case-by-case tuning with a principled remedy. Through a neural tangent kernel analysis, we prove that the standard kernel's spectral radius grows as $Ω(γ^2)$ with coupling strength $γ$, whereas block-diagonal Gauss-Newton (GN) preconditioning bounds it by the number of networks $S$, inde...
| Authors | Youngjae Park, Jaemin Kim, Junghwa Hong |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 14 August 2026 |
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