| A data driven method for computing quasipotentials B Lin, Q Li, W Ren Mathematical and Scientific Machine Learning, 652-670, 2022 | 14 | 2022 |
| Computing committor functions for the study of rare events using deep learning Q Li, B Lin, W Ren The Journal of Chemical Physics 151 (5), 2019 | 73 | 2019 |
| Computing committor functions for the study of rare events using deep learning with importance sampling Q Li, B Lin, W Ren | 1 | 2018 |
| Computing high-dimensional invariant distributions from noisy data B Lin, Q Li, W Ren Journal of Computational Physics 474, 111783, 2023 | 6 | 2023 |
| Computing the invariant distribution of randomly perturbed dynamical systems using deep learning B Lin, Q Li, W Ren Journal of Scientific Computing 91 (3), 77, 2022 | 9 | 2022 |
| Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning B Lin, Y Zhong, W Ren arXiv preprint arXiv:2404.05905, 2024 | | 2024 |
| Deep Learning Based Methods for the Study of Dynamical Systems L Bo PQDT-Global, 2021 | | 2021 |
| Deep Learning Method for Computing Committor Functions with Adaptive Sampling B Lin, W Ren arXiv preprint arXiv:2404.06206, 2024 | 1 | 2024 |
| Sparse identification of quasipotentials via a combined data-driven method B Lin, P Belardinelli arXiv preprint arXiv:2407.05050, 2024 | | 2024 |
