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Evidence record 12990 · automatically gathered

PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs

Physics-informed learning of partial differential equations (PDEs) has been dominated by multilayer perceptrons (MLPs), whose spectral bias and dense parameterization limit both accuracy and interpretability. Kolmogorov Arnold Networks (KANs) mitigate these limitations because their learnable spline activations are structurally aligned with the piecewise-polynomial bases of classical discretizations. However, the way a PDE is cast into a loss functional is as decisive as the choice of approximat

Record details

Published: 22 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 23 July 2026

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How to cite this record

ethics.ai (22 July 2026), “PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs,” evidence record 12990, https://ethics.ai/record/12990 (originally published by arXiv cs.LG).

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