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cs.LG2026★ 1 cited
Weak-Form Evolutionary Kolmogorov-Arnold Networks for Solving Partial Differential Equations
Bongseok Kim, Jiahao Zhang, Guang Lin
Partial differential equations (PDEs) form a central component of scientific computing. Among recent advances in deep learning, evolutionary neural networks have been developed to…
cs.LG2025
PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
Junyi Wu, Guang Lin
We propose PO-CKAN, a physics-informed deep operator framework based on Chunkwise Rational Kolmogorov--Arnold Networks (KANs), for approximating the solution operators of partial d…
cs.LG2025★ 1 cited
Physics Informed Constrained Learning of Dynamics from Static Data
Pengtao Dang, Tingbo Guo, Melissa Fishel +4
A physics-informed neural network (PINN) models the dynamics of a system by integrating the governing physical laws into the architecture of a neural network. By enforcing physical…