About Me
I am an Instructor of Mathematics at Department of Mathematics and the Program in Applied and Computational Mathematics (PACM), Princeton University. I obtained my Ph.D. degree of applied mathematics from PACM, Princeton University in June 2018, advised by Prof. Weinan E. Before that, I received my Bachelor degree from School of Mathematical Sciences, Peking University in July 2013.
My research draws inspiration from various disciplines of science and is devoted to solving highdimensional problems arising from scientific computing. In particular, I am interested in largescale molecular dynamics simulation, quantum manybody problem, highdimensional stochastic control, numerical methods of partial differential equations.
I did a research internship in DeepMind during the summer of 2017, under the mentorship of Thore Graepel.
Here are my CV and some related links: Google Scholar profile, ResearchGate profile.
News
Publications & Preprints

A class of dimensionalityfree metrics for the convergence of empirical measures,
Jiequn Han, Ruimeng Hu, Jihao Long,
arXiv preprint, (2021).
[arXiv]

An L2 analysis of reinforcement learning in high dimensions with kernel and neural network approximation,
Jihao Long, Jiequn Han, Weinan E,
arXiv preprint, (2021).
[arXiv]

Frameindependent vectorcloud neural network for nonlocal constitutive modelling on arbitrary grids,
XuHui Zhou, Jiequn Han, Heng Xiao,
arXiv preprint, (2021).
[arXiv]

Actorcritic method for high dimensional static HamiltonJacobiBellman partial differential equations based on neural networks,
Mo Zhou, Jiequn Han, Jianfeng Lu,
arXiv preprint, (2021).
[arXiv]

Recurrent neural networks for stochastic control problems with delay,
Jiequn Han, Ruimeng Hu,
arXiv preprint, (2021).
[arXiv]

Optimal policies for a pandemic: A stochastic game approach and a deep learning algorithm,
Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Hector D Ceniceros,
arXiv preprint, (2021).
[arXiv]

Learning nonlocal constitutive models with neural networks,
XuHui Zhou, Jiequn Han, Heng Xiao,
arXiv preprint, (2020).
[arXiv]

Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning,
Weinan E, Jiequn Han, Arnulf Jentzen,
arXiv preprint, (2020).
[arXiv]
[website]

Convergence of deep fictitious play for stochastic differential games,
Jiequn Han, Ruimeng Hu, Jihao Long,
arXiv preprint, (2020).
[arXiv]

Integrating machine learning with physicsbased modeling,
Weinan E, Jiequn Han, Linfeng Zhang,
arXiv preprint, (2020).
[arXiv]

Perturbed gradient descent with occupation time,
Xin Guo, Jiequn Han, Wenpin Tang,
arXiv preprint, (2020).
[arXiv]

Universal approximation of symmetric and antisymmetric functions,
Jiequn Han, Yingzhou Li, Lin Lin, Jianfeng Lu, Jiefu Zhang, Linfeng Zhang,
arXiv preprint, (2019).
[arXiv]

On the curse of memory in recurrent neural networks: approximation and optimization analysis,
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li,
International Conference on Learning Representations (ICLR), (2021).
[OpenReview]

Income and wealth distribution in macroeconomics: A continuoustime approach,
Yves Achdou, Jiequn Han, JeanMichel Lasry, PierreLouis Lions, Benjamin Moll,
The Review of Economic Studies (2021).
[journal]
[NBER]

Machine learning moment closures for accurate and efficient simulation of polydisperse evaporating sprays,
James B. Scoggins, Jiequn Han, Marc Massot,
AIAA Scitech 2021 Forum, 1786 (2021).
[proceedings]

Solving highdimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach,
Jiequn Han, Jianfeng Lu, Mo Zhou,
Journal of Computational Physics, 423, 109792 (2020).
[journal]
[arXiv]
[code]

Deep fictitious play for finding Markovian Nash equilibrium in multiagent games,
Jiequn Han, Ruimeng Hu,
Mathematical and Scientific Machine Learning Conferenc(MSML), PMLR 107:221245 (2020).
[proceedings]
[arXiv]

Convergence of the deep BSDE method for coupled FBSDEs,
Jiequn Han, Jihao Long,
Probability, Uncertainty and Quantitative Risk, 5(1), 133 (2020).
[journal]
[arXiv]

Uniformly accurate machine learningbased hydrodynamic models for kinetic equations,
Jiequn Han, Chao Ma, Zheng Ma, Weinan E,
Proceedings of the National Academy of Sciences, 116(44) 2198321991 (2019).
[journal]
[arXiv]

Solving manyelectron Schrödinger equation using deep neural networks,
Jiequn Han, Linfeng Zhang, Weinan E,
Journal of Computational Physics, 399, 108929 (2019).
[journal]
[arXiv]

A meanfield optimal control formulation of deep learning,
Weinan E, Jiequn Han, Qianxiao Li,
Research in the Mathematical Sciences, 6:10 (2019).
[journal]
[arXiv]

Endtoend symmetry preserving interatomic potential energy model for finite and extended systems,
Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, Weinan E,
Conference on Neural Information Processing Systems (NeurIPS), (2018).
[proceedings]
[arXiv]
[website]
[code]

Solving highdimensional partial differential equations using deep learning,
Jiequn Han, Arnulf Jentzen, Weinan E,
Proceedings of the National Academy of Sciences, 115(34), 85058510 (2018).
[journal]
[arXiv]
[code]

DeePCG: constructing coarsegrained models via deep neural networks,
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E,
The Journal of Chemical Physics, 149, 034101 (2018).
[journal]
[arXiv]
[website]

DeePMDkit: A deep learning package for manybody potential energy representation and molecular dynamics,
Han Wang, Linfeng Zhang, Jiequn Han, Weinan E,
Computer Physics Communications, 228, 178184 (2018).
[journal]
[arXiv]
[website]
[code]

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics,
Linfeng Zhang, Han Wang, Jiequn Han, Roberto Car, Weinan E,
Physical Review Letters 120(10), 143001 (2018).
[journal]
[arXiv]
[website]
[code]

Deep Potential: a general representation of a manybody potential energy surface,
Jiequn Han, Linfeng Zhang, Roberto Car, Weinan E,
Communications in Computational Physics, 23, 629–639 (2018).
[journal]
[arXiv]
[website]

Deep learningbased numerical methods for highdimensional parabolic partial differential equations and backward stochastic differential equations,
Weinan E, Jiequn Han, Arnulf Jentzen,
Communications in Mathematics and Statistics, 5, 349–380 (2017).
[journal]
[arXiv]
[code]

Deep learning approximation for stochastic control problems,
Jiequn Han, Weinan E,
Deep Reinforcement Learning Workshop, NIPS (2016).
[arXiv]

From microscopic theory to macroscopic theory: a systematic study on modeling for liquid crystals,
Jiequn Han, Yi Luo, Zhifei Zhang, Pingwen Zhang,
Archive for Rational Mechanics and Analysis, 215, 741–809 (2015).
[journal]
[arXiv]