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.
Characterizing possible failure modes in physics-informed neural networks
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ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.
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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.
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Deep Learning Alternatives of the Kolmogorov Superposition Theorem
ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.