Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion
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Publication:2671417
DOI10.1016/j.jcp.2022.111301OpenAlexW4280607878MaRDI QIDQ2671417
Inanc Senocak, Shamsulhaq Basir
Publication date: 3 June 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.14860
constrained optimizationpartial differential equationsaugmented Lagrangian methodresidual neural networksmulti-fidelity learningforward and inverse problems
Mathematical programming (90Cxx) Artificial intelligence (68Txx) Physiological, cellular and medical topics (92Cxx)
Related Items (6)
Multi-Fidelity Machine Learning Applied to Steady Fluid Flows ⋮ Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) ⋮ A symmetry group based supervised learning method for solving partial differential equations ⋮ Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations ⋮ Residual-based attention in physics-informed neural networks ⋮ Optimization of physics-informed neural networks for solving the nolinear Schrödinger equation
Uses Software
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