Block-diagonal Gauss-Newton preconditioning bounds the preconditioned NTK spectral radius by the number of networks independent of coupling strength, enabling coupling-robust accuracy in multiphysics PINNs via SOAP+GN.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
Block-diagonal Gauss-Newton preconditioning bounds the preconditioned NTK spectral radius by the number of networks independent of coupling strength, enabling coupling-robust accuracy in multiphysics PINNs via SOAP+GN.
- Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities