1. Theory-guided physicsinformed neural networks for boundary layer problems with singular perturbation;A Arzani;Journal of Computational Physics,2023
2. Flow over an espresso cup: inferring 3-d velocity and pressure fields from tomographic background oriented schlieren via physics-informed neural networks;S Cai;Journal of Fluid Mechanics,2021
3. A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation;W Cao;Physics of Fluids,2024
4. Multi-scale physics-informed neural networks for solving high reynolds number boundary layer flows based on matched asymptotic expansions;J Huang;Theoretical and Applied Mechanics Letters,2024
5. Extended physics-informed neural networks (xpinns): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations;A D Jagtap;Communications in Computational Physics,2020