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Instructor of Mathematics

Department of Mathematics

Princeton University

Waptrick mp4 videos. 1007 Fine Hall, Washington Road

Princeton, NJ 08544 USA

Email: jiequnh (at) princeton (dot) edu

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.

**07/2020:**I co-organized (with Qi Gong and Wei Kang) the minisymposium on the intersection of optimal control and machine learning at the SIAM annual meeting. Details can be found here.**12/2019:**Deep BSDE solver is updated to support TensorFlow 2.0.

**Learning nonlocal constitutive models with neural networks,**

Xu-Hui Zhou, Jiequn Han, Heng Xiao,

arXiv preprint, (2020). [arXiv]**On the curse of memory in recurrent neural networks: approximation and optimization analysis,**

Zhong Li, Jiequn Han, Weinan E, Qianxiao Li,

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 physics-based 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 anti-symmetric functions,**

Jiequn Han, Yingzhou Li, Lin Lin, Jianfeng Lu, Jiefu Zhang, Linfeng Zhang,

arXiv preprint, (2019). [arXiv]**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]**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]**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]**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]**Solving many-electron Schrödinger equation using deep neural networks,**

Jiequn Han, Linfeng Zhang, Weinan E,

Journal of Computational Physics, 399, 108929 (2019). [journal] [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]

#### The Nature of Science and Physics

#### Kinematics

#### Two-Dimensional Kinematics

#### Dynamics: Force and Newton's Laws of Motion

#### Further Applications of Newton's Laws: Friction, Drag, and Elasticity

#### Uniform Circular Motion and Gravitation

#### Work, Energy, and Energy Resources

#### Linear Momentum and Collisions

#### Statics and Torque

#### Rotational Motion and Angular Momentum

#### Fluid Statics

#### Fluid Dynamics and Its Biological and Medical Applications

#### Temperature, Kinetic Theory, and the Gas Laws

#### Heat and Heat Transfer Methods

#### Thermodynamics

#### Oscillatory Motion and Waves

#### Physics of Hearing

#### Electric Charge and Electric Field

#### Electric Potential and Electric Field

#### Electric Current, Resistance, and Ohm's Law

#### Circuits and DC Instruments

#### Magnetism

#### Electromagnetic Induction, AC Circuits, and Electrical Technologies

#### Electromagnetic Waves

#### Geometric Optics

#### Vision and Optical Instruments

#### Wave Optics

#### Special Relativity

#### Introduction to Quantum Physics

#### Atomic Physics

#### Radioactivity and Nuclear Physics

#### Medical Applications of Nuclear Physics

#### Particle Physics

#### Frontiers of Physics

#### Appendices

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