| Solving high-dimensional partial differential equations using deep learning J Han, A Jentzen, W E Proceedings of the National Academy of Sciences 115 (34), 8505-8510, 2018 | 1768 | 2018 |
| Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics L Zhang, J Han, H Wang, R Car, W E Physical review letters 120 (14), 143001, 2018 | 1483 | 2018 |
| DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics H Wang, L Zhang, J Han, W E Computer Physics Communications 228, 178-184, 2018 | 1047 | 2018 |
| Income and wealth distribution in macroeconomics: A continuous-time approach Y Achdou, J Han, JM Lasry, PL Lions, B Moll The review of economic studies 89 (1), 45-86, 2022 | 713* | 2022 |
| Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations W E, J Han, A Jentzen Communications in Mathematics and Statistics 5 (4), 349-380, 2017 | 621* | 2017 |
| End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems L Zhang, J Han, H Wang, W Saidi, R Car, W E Advances in Neural Information Processing Systems, 4436-4446, 2018 | 447 | 2018 |
| Deep potential: a general representation of a many-body potential energy surface J Han, L Zhang, R Car, W E Communications in Computational Physics 23 (3), 629-639, 2018 | 238 | 2018 |
| Deep learning approximation for stochastic control problems J Han, W E Advances in Neural Information Processing Systems, Deep Reinforcement …, 2016 | 220* | 2016 |
| Solving many-electron Schrödinger equation using deep neural networks J Han, L Zhang, W E Journal of Computational Physics 399, 108929, 2019 | 205 | 2019 |
| DeePCG: Constructing coarse-grained models via deep neural networks L Zhang, J Han, H Wang, R Car The Journal of chemical physics 149 (3), 2018 | 182 | 2018 |
| Convergence of the deep BSDE method for coupled FBSDEs J Han, J Long Probability, Uncertainty and Quantitative Risk 5 (1), 5, 2020 | 154 | 2020 |
| A mean-field optimal control formulation of deep learning W E, J Han, Q Li Research in the Mathematical Sciences 6 (1), 10, 2019 | 152* | 2019 |
| Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning W E, J Han, A Jentzen Nonlinearity 35 278, 2021 | 149* | 2021 |
| DeePMD-kit v2: A software package for deep potential models J Zeng, D Zhang, D Lu, P Mo, Z Li, Y Chen, M Rynik, L Huang, Z Li, S Shi, ... The Journal of Chemical Physics 159 (5), 2023 | 95 | 2023 |
| Uniformly accurate machine learning-based hydrodynamic models for kinetic equations J Han, C Ma, Z Ma, W E Proceedings of the National Academy of Sciences 116 (44), 21983-21991, 2019 | 84 | 2019 |
| From microscopic theory to macroscopic theory: a systematic study on static modeling for liquid crystals J Han, Y Luo, W Wang, P Zhang Archive for Rational Mechanics and Analysis 215 (3), 741–809, 2013 | 84* | 2013 |
| Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach J Han, J Lu, M Zhou Journal of Computational Physics 423, 109792, 2020 | 79 | 2020 |
| Deep fictitious play for finding Markovian Nash equilibrium in multi-agent games J Han, R Hu Mathematical and scientific machine learning, 221-245, 2020 | 48 | 2020 |
| Neural-network quantum states for periodic systems in continuous space G Pescia, J Han, A Lovato, J Lu, G Carleo Physical Review Research 4 (2), 023138, 2022 | 47 | 2022 |
| Machine-learning-assisted modeling W E, J Han, L Zhang Physics Today 74 (7), 36-41, 2021 | 46* | 2021 |
Weinan EProfessor of Mathematics, Princeton University在 math.princeton.edu 的电子邮件经过验证
