1. Physics-informed machine learning;George Em Karniadakis;Nature Reviews Physics,2021
2. B-PINNs: Bayesian physicsinformed neural networks for forward and inverse PDE problems with noisy data;Liu Yang;Journal of Computational Physics,2021
3. Bayesian physics informed neural networks for real-world nonlinear dynamical systems;Kevin Linka;Computer Methods in Applied Mechanics and Engineering,2022
4. Correcting model misspecification in physics-informed neural networks (PINNs);Zongren Zou;Journal of Computational Physics,2024