About Me
I am a fifthyear Ph.D. student of the Program in Applied and Computational Mathematics (PACM) at Princeton University, advised by Prof. Weinan E. 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. Before coming to Princeton, I received my Bachelor degree from School of Mathematical Sciences, Peking University in July 2013.
Here are some related links: Google Scholar profile, ResearchGate profile.
Publications & Preprints

"DeePCG: constructing coarsegrained models via deep neural networks",
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E,
ArXiv preprint, (2018).
[arXiv]

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

"Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics",
Linfeng Zhang, Han Wang, Jiequn Han, Roberto Car, Weinan E,
to appear in Physical Review Letters, (2018).
[arXiv]

"Overcoming the curse of dimensionality: Solving highdimensional partial differential equations using deep learning",
Jiequn Han, Arnulf Jentzen, Weinan E,
ArXiv preprint, (2017).
[arXiv]
[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 (2018), pp. 629–639.
[journal]

"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 (2017), pp. 349–380.
[journal]
[arXiv]
[code]

"Income and wealth distribution in macroeconomics: A continuoustime approach",
Yves Achdou, Jiequn Han, JeanMichel Lasry, PierreLouis Lions, Benjamin Moll,
National Bureau of Economic Research, (2017).
[DOI]

"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 (2015), pp. 741–809.
[journal]
[arXiv]