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Online Physics Courses and Programs. Get an introduction to physics with online courses from major universities and institutions worldwide. Edx offers both individual courses and advanced programs designed to help you learn about physics in an engaging and effective online learning environment complete with video tutorials, quizzes and more. The Activity-Based Physics website is the work of a multi-university collaborative team seeking to make introductory physics courses more effective and exciting at the high school and college levels. The goal is to develop new instructional strategies and materials, using activity-based models, informed by extensive classroom testing.

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

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.

*physics 12sph4umr.


  • 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.

Publications & Preprints

  • 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]

*physics 12sph4umr.'s Learning Website Examples

Table of Contents

*physics 12sph4umr.

Free Learning Website

  • 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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