Jiequn Han
Jiequn Han
Verified email at princeton.edu - Homepage
Cited by
Cited by
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
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
Income and wealth distribution in macroeconomics: A continuous-time approach
Y Achdou, J Han, JM Lasry, PL Lions, B Moll
National Bureau of Economic Research Working Paper Series, 2017
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
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
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
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
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
DeePCG: Constructing coarse-grained models via deep neural networks
L Zhang, J Han, H Wang, R Car, W E
The Journal of chemical physics 149 (3), 034101, 2018
Deep learning approximation for stochastic control problems
J Han, W E
NIPS 2016, Deep Reinforcement Learning Workshop, 2016
A mean-field optimal control formulation of deep learning
W E, J Han, Q Li
Research in the Mathematical Sciences 6 (1), 10, 2019
Solving many-electron Schrödinger equation using deep neural networks
J Han, L Zhang, W E
Journal of Computational Physics 399, 108929, 2019
Convergence of the deep BSDE method for coupled FBSDEs
J Han, J Long
Probability, Uncertainty and Quantitative Risk 5 (1), 1-33, 2020
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
Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach
J Han, J Lu, M Zhou
arXiv preprint arXiv:2002.02600, 2020
Universal approximation of symmetric and anti-symmetric functions
J Han, Y Li, L Lin, J Lu, J Zhang, L Zhang
arXiv preprint arXiv:1912.01765, 2019
Deep fictitious play for finding Markovian Nash equilibrium in multi-agent games
J Han, R Hu
Mathematical and Scientific Machine Learning, 221-245, 2020
Convergence of deep fictitious play for stochastic differential games
J Han, R Hu, J Long
arXiv preprint arXiv:2008.05519, 2020
Integrating Machine Learning with Physics-Based Modeling
W E, J Han, L Zhang
arXiv preprint arXiv:2006.02619, 2020
Perturbed gradient descent with occupation time
X Guo, J Han, W Tang
arXiv preprint arXiv:2005.04507, 2020
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