Jiequn Han (韩劼群) Instructor of Mathematics Department of Mathematics Princeton University 1007 Fine Hall, Washington Road Princeton, NJ 08544 USA Email: jiequnh (at) princeton (dot) edu

---

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.

---

Publications & Preprints

• "Convergence of the Deep BSDE Method for Coupled FBSDEs",
  Jiequn Han, Jihao Long,
  arXiv preprint (2018). [arXiv]

• "Solving many-electron Schrödinger equation using deep neural networks",
  Jiequn Han, Linfeng Zhang, Weinan E,
  arXiv preprint (2018). [arXiv]

• "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]

• "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]

• "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]

• "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]

• "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]

• "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 many-body potential energy surface",
  Jiequn Han, Linfeng Zhang, Roberto Car, Weinan E,
  Communications in Computational Physics, 23, 629–639 (2018). [journal] [arXiv] [website]

• "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]

---