Jiequn Han (韩劼群) Ph.D. Candidate Program in Applied and Computational Mathematics Princeton University 218 Fine Hall, Washington Road Princeton, NJ 08544 USA Email: jiequnh (at) princeton (dot) edu

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About Me

I am a fifth-year 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 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. 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.

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Publications & Preprints

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

• "Solving high-dimensional partial differential equations using deep learning",
  Jiequn Han, Arnulf Jentzen, Weinan E,
  ArXiv preprint (2017). [arXiv] [code]

• "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] [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] [code]

• "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]

• "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]

• "Income and wealth distribution in macroeconomics: A continuous-time approach",
  Yves Achdou, Jiequn Han, Jean-Michel Lasry, Pierre-Louis 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, 741–809 (2015). [journal] [arXiv]

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