Phase-shift transferable neural networks achieve high-precision approximation of high-frequency functions and PDE solutions.
Stac ked networks improve physics-informed training: Applications to neural networks and deep operator netw orks
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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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High-Precision Phase-Shift Transferable Neural Networks for High-Frequency Function Approximation and PDE Solution
Phase-shift transferable neural networks achieve high-precision approximation of high-frequency functions and PDE solutions.
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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.