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 high-dimensional problems arising from scientific computing. In particular, I am interested in large-scale molecular dynamics simulation, quantum many-body problem, high-dimensional 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
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Actor-critic method for high dimensional static Hamilton--Jacobi--Bellman partial differential equations based on neural networks,
Mo Zhou, Jiequn Han, Jianfeng Lu,
arXiv preprint, (2021).
[arXiv]
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Recurrent neural networks for stochastic control problems with delay,
Jiequn Han, Ruimeng Hu,
arXiv preprint, (2021).
[arXiv]
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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]
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Learning nonlocal constitutive models with neural networks,
Xu-Hui Zhou, Jiequn Han, Heng Xiao,
arXiv preprint, (2020).
[arXiv]
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Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning,
Weinan E, Jiequn Han, Arnulf Jentzen,
arXiv preprint, (2020).
[arXiv]
[website]
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Convergence of deep fictitious play for stochastic differential games,
Jiequn Han, Ruimeng Hu, Jihao Long,
arXiv preprint, (2020).
[arXiv]
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Integrating machine learning with physics-based modeling,
Weinan E, Jiequn Han, Linfeng Zhang,
arXiv preprint, (2020).
[arXiv]
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Perturbed gradient descent with occupation time,
Xin Guo, Jiequn Han, Wenpin Tang,
arXiv preprint, (2020).
[arXiv]
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Universal approximation of symmetric and anti-symmetric functions,
Jiequn Han, Yingzhou Li, Lin Lin, Jianfeng Lu, Jiefu Zhang, Linfeng Zhang,
arXiv preprint, (2019).
[arXiv]
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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]
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Income and wealth distribution in macroeconomics: A continuous-time approach,
Yves Achdou, Jiequn Han, Jean-Michel Lasry, Pierre-Louis Lions, Benjamin Moll,
The Review of Economic Studies (2021).
[journal]
[NBER]
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Solving high-dimensional 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]
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Deep fictitious play for finding Markovian Nash equilibrium in multi-agent games,
Jiequn Han, Ruimeng Hu,
Mathematical and Scientific Machine Learning Conferenc(MSML), PMLR 107:221-245 (2020).
[proceedings]
[arXiv]
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Convergence of the deep BSDE method for coupled FBSDEs,
Jiequn Han, Jihao Long,
Probability, Uncertainty and Quantitative Risk, 5(1), 1-33 (2020).
[journal]
[arXiv]
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Uniformly accurate machine learning-based hydrodynamic models for kinetic equations,
Jiequn Han, Chao Ma, Zheng Ma, Weinan E,
Proceedings of the National Academy of Sciences, 116(44) 21983-21991 (2019).
[journal]
[arXiv]
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Solving many-electron Schrödinger equation using deep neural networks,
Jiequn Han, Linfeng Zhang, Weinan E,
Journal of Computational Physics, 399, 108929 (2019).
[journal]
[arXiv]
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A mean-field optimal control formulation of deep learning,
Weinan E, Jiequn Han, Qianxiao Li,
Research in the Mathematical Sciences, 6:10 (2019).
[journal]
[arXiv]
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End-to-end symmetry preserving inter-atomic 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]
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Solving high-dimensional partial differential equations using deep learning,
Jiequn Han, Arnulf Jentzen, Weinan E,
Proceedings of the National Academy of Sciences, 115(34), 8505-8510 (2018).
[journal]
[arXiv]
[code]
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DeePCG: constructing coarse-grained 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]
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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics,
Han Wang, Linfeng Zhang, Jiequn Han, Weinan E,
Computer Physics Communications, 228, 178-184 (2018).
[journal]
[arXiv]
[website]
[code]
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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]
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Deep Potential: a general representation of a many-body potential energy surface,
Jiequn Han, Linfeng Zhang, Roberto Car, Weinan E,
Communications in Computational Physics, 23, 629–639 (2018).
[journal]
[arXiv]
[website]
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Deep learning-based numerical methods for high-dimensional 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]
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Deep learning approximation for stochastic control problems,
Jiequn Han, Weinan E,
Deep Reinforcement Learning Workshop, NIPS (2016).
[arXiv]
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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]