Physics-Informed Learning

Physics-constrained learning of PDE systems with uncertainty quantified port-Hamiltonian models L4DC 2024 Physics-informed machine learning as a kernel method COLT 2024 Guiding continuous operator learning through Physics-based boundary constraints ICLR 2023 Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography ICLR 2023 PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification NeurIPS 2023 Auto-Physics-Encoder: Using Physics-Informed Latent Layer Two-Way Physics Flow for Monitoring Systems with Unobservability ACML 2022 Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning ICLR 2022 Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality ICLR 2022 Physics-Informed Implicit Representations of Equilibrium Network Flows NeurIPS 2022 A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Partial Differential Equations COLT 2021 SPDE-Net: Neural Network based prediction of stabilization parameter for SUPG technique ACML 2021 Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints NeurIPS 2020