Physics-Informed Neural Networks with Hard Constraints for Inverse Design

2021/01/01 by Lu Lu, Raphaël Pestourie, Wenjie Yao +3 · 63 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Physics and Astronomy · #Advanced Electron Microscopy Techniques and Applications #Model Reduction and Neural Networks #Topology Optimization in Engineering

paper · doi:10.1137/21m1397908

openalex publication_date 2021/01/01 · openalex created_date 2021/02/15 · openalex updated_date 2026/07/29

Citations

• Artificial Neural Networks for Solving Ordinary and Partial Differential Equations
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
• Physics-informed machine learning

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