A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs
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Publication:2681136
DOI10.1016/j.jcp.2023.111912OpenAlexW4315477618MaRDI QIDQ2681136
Shandian Zhe, Michael Penwarden, Robert M. Kirby, Akil C. Narayan
Publication date: 10 February 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2110.13361
Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Approximations and expansions (41Axx)
Related Items (3)
Multi-Fidelity Machine Learning Applied to Steady Fluid Flows ⋮ Branched latent neural maps ⋮ Solving seepage equation using physics-informed residual network without labeled data
Uses Software
Cites Work
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