| A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method S Rezaei, A Harandi, A Moeineddin, BX Xu, S Reese Computer Methods in Applied Mechanics and Engineering 401, 115616, 2022 | 78 | 2022 |
| Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains A Harandi, A Moeineddin, M Kaliske, S Reese, S Rezaei International Journal for Numerical Methods in Engineering 125 (4), e7388, 2024 | 14 | 2024 |
| Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations S Rezaei, A Moeineddin, M Kaliske, M Apel arXiv preprint arXiv:2401.02363, 2024 | 3 | 2024 |
| Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks S Rezaei, A Moeineddin, A Harandi Computational Mechanics, 1-34, 2024 | 2 | 2024 |
| Physics-informed neural networks applied to catastrophic creeping landslides A Moeineddin, C Seguí, S Dueber, R Fuentes Landslides 20 (9), 1853-1863, 2023 | 1 | 2023 |
| Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN S Rezaei, A Moeineddin, A Harandi arXiv preprint arXiv:2304.06044, 2023 | 1 | 2023 |
| Phase-field fracture model solved by a mixed formulation for physics-informed neural networks A Harandi, S Rezaei, A Moeineddin, T Brepols, S Reese | | |
