| Data-driven discovery of governing equations for fluid dynamics based on molecular simulation J Zhang, W Ma Journal of Fluid Mechanics 892, A5, 2020 | 72 | 2020 |
| Non-intrusive reduced order modeling for flowfield reconstruction based on residual neural network W Ma, J Zhang, J Yu Acta Astronautica 183, 346-362, 2021 | 16 | 2021 |
| Using gene expression programming to discover macroscopic governing equations hidden in the data of molecular simulations H Xing, J Zhang, W Ma, D Wen Physics of Fluids 34 (5), 2022 | 10 | 2022 |
| Simulation of rarefied gas flows using physics-informed neural network combined with discrete velocity method L Zhang, W Ma, Q Lou, J Zhang Physics of Fluids 35 (7), 2023 | 3 | 2023 |
| Dimensional homogeneity constrained gene expression programming for discovering governing equations from noisy and scarce data W Ma, J Zhang, K Feng, H Xing, D Wen arXiv preprint arXiv:2211.09679, 2022 | 2 | 2022 |
| Dimensional homogeneity constrained gene expression programming for discovering governing equations W Ma, J Zhang, K Feng, H Xing, D Wen Journal of Fluid Mechanics 985, A12, 2024 | | 2024 |
Jun Zhang (张俊)Beihang UniversityVerified email at buaa.edu.cn
