The Hard-Constraint PINNs for Interface Optimal Control Problems

2025/05/06 by Ming-Chih Lai, Yongcun Song, Xiaoming Yuan +2 · 6 citations
Engineering · Physics and Astronomy · #Advanced Control Systems Optimization #Advanced Numerical Methods in Computational Mathematics #Model Reduction and Neural Networks

paper · doi:10.1137/23m1601249

openalex publication_date 2025/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

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
• Physics-Informed Neural Networks with Hard Constraints for Inverse Design
• Physics-informed machine learning
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
• Optimal control of PDEs using physics-informed neural networks

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• A priori error estimates for stable generalized finite element discretization of parabolic interface optimal control problems
• Numerical Analysis of Unsupervised Learning Approaches for Parameter Identification in PDEs
• Hybrid Boundary Physics-Informed Neural Networks for Solving Navier-Stokes Equations with Complex Boundary
• WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs with Physics-Informed Neural Networks