Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation
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Publication:2106998
DOI10.1016/J.JCP.2022.111768OpenAlexW4308978381MaRDI QIDQ2106998
Roshan M. D'Souza, Amirhossein Arzani, Kevin W. Cassel
Publication date: 29 November 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111768
asymptotic expansiondeep learningconvective transportdata-driven modelingscientific machine learning
Basic methods in fluid mechanics (76Mxx) Incompressible viscous fluids (76Dxx) Physiological, cellular and medical topics (92Cxx)
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Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems ⋮ Less Emphasis on Hard Regions: Curriculum Learning of PINNs for Singularly Perturbed Convection-Diffusion-Reaction Problems
Uses Software
Cites Work
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