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 machine learning
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SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
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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Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
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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.
- Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities