Block-diagonal Gauss–Newton and SOAP keep PINN NTK spectral radius independent of coupling strength, restoring accuracy that Adam loses as multiphysics coupling grows.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2representative citing papers
citing papers explorer
-
Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
Block-diagonal Gauss–Newton and SOAP keep PINN NTK spectral radius independent of coupling strength, restoring accuracy that Adam loses as multiphysics coupling grows.
- Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities