June 6, 2024, 4:42 a.m. | Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, Zongren Zou, George Em Karniadakis

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02917v1 Announce Type: new
Abstract: Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning models (PIKANs) and deep operator models (DeepOKANs) for solving differential equations for forward and inverse problems. In particular, we compare them with physics-informed neural networks (PINNs) and deep operator networks (DeepONets), which are based on the standard MLP representation. We find that although the original KANs based on the B-splines parameterization lack accuracy …

abstract alternative arxiv comparison construct cs.lg differential fair kan machine machine learning machine learning models mlp networks physics physics.comp-ph physics-informed representation type

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