Introduces pinn-gym benchmark demonstrating that low curve error in physics-informed surrogates frequently fails to yield useful design selections across per-material, pooled, and cross-material settings.
From PINNs to PIKANs: Recent advances in physics-informed mac hine learning
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A PINN approach learns galactic gravitational potentials from acceleration data, achieving sub-percent errors on simulations while outperforming analytic models and retaining interpretability via structured priors.
SPLIT-PINN infers drift fields in Liouville transport equations from data using marginal corrections and orthogonality constraints to enable probabilistic predictions of microstructural evolution across polycrystal realizations.
Modified PINNs using bounded sigmoid activation, log-scale MSE loss, and added conservation terms recover Marshak wave dynamics matching Implicit Monte Carlo reference solutions.
A systematic review of Kolmogorov-Arnold Networks that maps their relation to Kolmogorov superposition theory, MLPs, and kernels, examines basis-function design choices, summarizes performance advances, and supplies a practitioner's selection guide plus open challenges.
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A Practitioner's Guide to Kolmogorov-Arnold Networks
A systematic review of Kolmogorov-Arnold Networks that maps their relation to Kolmogorov superposition theory, MLPs, and kernels, examines basis-function design choices, summarizes performance advances, and supplies a practitioner's selection guide plus open challenges.