AI RESEARCH
Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
arXiv CS.LG
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ArXi:2505.07765v3 Announce Type: replace-cross We propose a novel framework for solving nonlinear PDEs using sparse radial basis function (RBF) networks. Sparsity-promoting regularization is employed to prevent over-parameterization and reduce redundant features. This work is motivated by longstanding challenges in traditional RBF collocation methods, along with the limitations of physics-informed neural networks (PINNs) and Gaussian process (GP) approaches, aiming to blend their respective strengths in a unified framework.