| PDE-Net: Learning pdes from data Z Long, Y Lu, X Ma, B Dong arXiv preprint arXiv:1710.09668, 2017 | 872 | 2017 |
| PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network Z Long, Y Lu, B Dong Journal of Computational Physics 399, 108925, 2019 | 587 | 2019 |
| Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning Y Wang, Z Shen, Z Long, B Dong arXiv preprint arXiv:1905.11079, 2019 | 52 | 2019 |
| PDE-net: Learning PDEs from data (2017) Z Long, Y Lu, X Ma, B Dong arXiv preprint arXiv:1710.09668, 0 | 12 | |
| Rode-net: learning ordinary differential equations with randomness from data J Liu, Z Long, R Wang, J Sun, B Dong arXiv preprint arXiv:2006.02377, 2020 | 7 | 2020 |
| Computational Graph Representation of Equations System Constructors in Hierarchical Circuit Simulation Z Long, L Li, L Han, X Meng, C Ding, R Li, W Jiang, F Ding, J Yue, Z Li, ... arXiv preprint arXiv:2407.04206, 2024 | | 2024 |
| A Direct Numerical Simulation (DNS) Study on the Effect of Particle Configuration on Drag Y Mo, Z Long, P Zhang COMMUNICATIONS IN COMPUTATIONAL PHYSICS 30 (3), 799-819, 2021 | | 2021 |
Bin DongBeijing International Center for Mathematical Research, Peking University在 math.pku.edu.cn 的电子邮件经过验证
