| Physics-informed machine learning for reduced-order modeling of nonlinear problems W Chen, Q Wang, JS Hesthaven, C Zhang Journal of computational physics 446, 110666, 2021 | 207 | 2021 |
| Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model W Chen, Y Fu, P Stinis Journal of Power Sources 584, 233548, 2023 | 10 | 2023 |
| A multidomain multigrid pseudospectral method for incompressible flows W Chen, Y Ju, C Zhang Numerical Heat Transfer, Part B: Fundamentals 74 (1), 415-431, 2018 | 9 | 2018 |
| Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations W Chen, P Stinis Journal of Computational Physics 498, 112683, 2024 | 8 | 2024 |
| A parallel inverted dual time stepping method for unsteady incompressible fluid flow and heat transfer problems W Chen, Y Ju, C Zhang Computer Physics Communications 260, 107325, 2021 | 6 | 2021 |
| Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks W Chen, AA Howard, P Stinis arXiv preprint arXiv:2407.01613, 2024 | 5 | 2024 |
| Physics-informed machine learning of the correlation functions in bulk fluids W Chen, P Gao, P Stinis Physics of Fluids 36 (1), 2024 | 4 | 2024 |
| A collocated-grid spectral difference method for compressible flows W Chen, Y Ju, C Zhang Computers & Fluids 196, 104341, 2020 | 4 | 2020 |
| Parallel-in-time-space Chebyshev pseudospectral method for unsteady fluid flows W Chena, Y Jua, C Zhanga | | |
Jan S HesthavenProfessor of Mathematics and Provost, Ecole Polytechnique Federale de Lausanne (EPFL)在 epfl.ch 的电子邮件经过验证
