Jiequn Han

发表

J un 2 01 8 DeePCG : constructing coarse-grained models via deep neural networks

Linfeng Zhang, Han Wang, Jiequn Han, 2018 .

Deep Potential: a general representation of a many-body potential energy surface

E. Weinan, R. Car, Linfeng Zhang, 2017, 1707.01478.

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems

E Weinan, Han Wang, Linfeng Zhang, 2018, NeurIPS.

Uniformly accurate machine learning-based hydrodynamic models for kinetic equations

Chao Ma, Weinan E, Jiequn Han, 2019, Proceedings of the National Academy of Sciences.

Recurrent Neural Networks for Stochastic Control Problems with Delay pdf

Ruimeng Hu, Jiequn Han, Jiequn Han, 2021, Math. Control. Signals Syst..

Learning nonlocal constitutive models with neural networks

Heng Xiao, Jiequn Han, Xu-Hui Zhou, 2020, 2010.10491.

Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence

Jiequn Han, Ruimeng Hu, Jihao Long, 2022, ArXiv.

Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm pdf

Ruimeng Hu, Yao Xuan, Jiequn Han, 2020, ArXiv.

Solving optimal control of rigid-body dynamics with collisions using the hybrid minimum principle

E. Weinan, Weihua Hu, Jiequn Han, 2022, 2205.08622.

Empowering Optimal Control with Machine Learning: A Perspective from Model Predictive Control

E. Weinan, Jiequn Han, Jihao Long, 2022, IFAC-PapersOnLine.

Pandemic Control, Game Theory and Machine Learning

H. D. Ceniceros, Jiequn Han, Ruimeng Hu, 2022, 2208.08646.

A class of dimension-free metrics for the convergence of empirical measures

Jiequn Han, Ruimeng Hu, Jihao Long, 2021, Stochastic Processes and their Applications.

A Class of Dimensionality-free Metrics for the Convergence of Empirical Measures

Jiequn Han, Ruimeng Hu, Jihao Long, 2021, ArXiv.

Frame invariance and scalability of neural operators for partial differential equations

Heng Xiao, Jiequn Han, M. Zafar, 2021, Communications in Computational Physics.

Initial Value Problem Enhanced Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks

E. Weinan, Weihua Hu, Jiequn Han, 2022, 2209.04078.

Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning

Arnulf Jentzen, Weinan E, Jiequn Han, 2020, Nonlinearity.

Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?

Yaofeng Desmond Zhong, Jiequn Han, Georgia Olympia Brikis, 2022, ArXiv.

Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence

Jiequn Han, Ruimeng Hu, Jihao Long, 2022, ArXiv.

An equivariant neural operator for developing nonlocal tensorial constitutive models

Heng Xiao, Jiequn Han, Xueqing Zhou, 2022, J. Comput. Phys..

From Microscopic Theory to Macroscopic Theory: a Systematic Study on Modeling for Liquid Crystals

Pingwen Zhang, Wei Wang, Zhifei Zhang, 2014, Archive for Rational Mechanics and Analysis.

Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control

Jiequn Han, Yue Zhao, 2022, 2211.15930.

Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations

Jiequn Han, 2018 .

A Neural Network Warm-Start Approach for the Inverse Acoustic Obstacle Scattering Problem

Mo Zhou, Jiequn Han, C. Borges, 2022, ArXiv.

Universal approximation of symmetric and anti-symmetric functions

Yingzhou Li, Jianfeng Lu, Lin Lin, 2019, Communications in Mathematical Sciences.

DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks

E. Weinan, Jiequn Han, Yucheng Yang, 2021, SSRN Electronic Journal.

Convergence of the deep BSDE method for coupled FBSDEs

Jiequn Han, Jihao Long, Jiequn Han, 2018, Probability, Uncertainty and Quantitative Risk.

Perturbed gradient descent with occupation time pdf

Xin Guo, Wenpin Tang, Jiequn Han, 2020, ArXiv.

Escaping Saddle Points Efficiently with Occupation-Time-Adapted Perturbations

Wenpin Tang, Jiequn Han, Xin Guo, 2020, 2005.04507.

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

E Weinan, Han Wang, Linfeng Zhang, 2017, Physical review letters.

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics pdf

E Weinan, Han Wang, Linfeng Zhang, 2017, Comput. Phys. Commun..

Neural-Network Quantum States for Periodic Systems in Continuous Space

Jianfeng Lu, G. Carleo, Jiequn Han, 2021, 2112.11957.

A mean-field optimal control formulation of deep learning

W. E, Qianxiao Li, Jiequn Han, 2018, Research in the Mathematical Sciences.

A mean-field optimal control formulation of deep learning

E Weinan, Qianxiao Li, Jiequn Han, 2018, Research in the Mathematical Sciences.

From Microscopic Theory to Macroscopic Theory: a Systematic Study on Modeling for Liquid Crystals

Wei Wang, Pingwen Zhang, Jiequn Han, 2013, 1305.4889.

A PDE-free, neural network-based eddy viscosity model coupled with RANS equations

Heng Xiao, R. Dwight, Jiequn Han, 2022, International Journal of Heat and Fluid Flow.

Solving high-dimensional partial differential equations using deep learning

Arnulf Jentzen, Weinan E, Jiequn Han, 2017, Proceedings of the National Academy of Sciences.

Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning pdf

E Weinan, Arnulf Jentzen, Jiequn Han, 2020, Nonlinearity.

Integrating Machine Learning with Physics-Based Modeling pdf

E Weinan, Linfeng Zhang, Jiequn Han, 2020, ArXiv.

VCNN-e: A vector-cloud neural network with equivariance for emulating Reynolds stress transport equations

Heng Xiao, Jiequn Han, Xueqing Zhou, 2022 .

Solving many-electron Schrödinger equation using deep neural networks

E Weinan, Linfeng Zhang, Jiequn Han, 2018, J. Comput. Phys..

A Machine Learning Enhanced Algorithm for the Optimal Landing Problem

E. Weinan, Weihua Hu, Jiequn Han, 2022, 2203.06753.

Reinforcement Learning with Function Approximation: From Linear to Nonlinear

Jiequn Han, Jihao Long, 2023, arXiv.org.

Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space

Jiequn Han, Jihao Long, 2021, Journal of Machine Learning.

An L2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation

W. E, E. Weinan, Jiequn Han, 2021, CSIAM Transactions on Applied Mathematics.

DeePMD-kit v2: A software package for deep potential models

Rhys E. A. Goodall, E. Weinan, D. York, 2023, The Journal of chemical physics.

DeePCG: Constructing coarse-grained models via deep neural networks.

Linfeng Zhang, Han Wang, Weinan E, 2018, The Journal of chemical physics.

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

E. Weinan, Qianxiao Li, Jiequn Han, 2022, J. Mach. Learn. Res..

Convergence of Deep Fictitious Play for Stochastic Differential Games pdf

Ruimeng Hu, Jiequn Han, Jihao Long, 2020, ArXiv.

Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations

E Weinan, Arnulf Jentzen, Jiequn Han, 2017, Communications in Mathematics and Statistics.

Deep Learning Approximation for Stochastic Control Problems pdf

E Weinan, Jiequn Han, W. E, 2016, ArXiv.

Frame-independent vector-cloud neural network for nonlocal constitutive modelling on arbitrary grids pdf

Heng Xiao, Jiequn Han, Xu-Hui Zhou, 2021, Computer Methods in Applied Mechanics and Engineering.

Machine-learning-assisted modeling

Weinan E, Jiequn Han, Linfeng Zhang, 2021, Physics Today.

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis pdf

E Weinan, Qianxiao Li, Jiequn Han, 2020, ICLR.

Income and Wealth Distribution in Macroeconomics: A Continuous-Time Approach

Pierre-Louis Lions, Yves Achdou, Jean-Michel Lasry, 2017, The Review of Economic Studies.

Learning nonlocal constitutive models with neural networks

Heng Xiao, Jiequn Han, Xueqing Zhou, 2020, 2010.10491.

Recurrent neural networks for stochastic control problems with delay

Ruimeng Hu, Jiequn Han, Jiequn Han, 2021, Mathematics of Control, Signals, and Systems.

Actor-Critic Method for High Dimensional Static Hamilton-Jacobi-Bellman Partial Differential Equations based on Neural Networks pdf

Jianfeng Lu, Jiequn Han, Mo Zhou, 2021, SIAM J. Sci. Comput..

Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time

Zhaoran Wang, Weichen Wang, Zhuoran Yang, 2020, ICML.

Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach

Jianfeng Lu, Jiequn Han, Mo Zhou, 2020, J. Comput. Phys..

Heterogeneous Agent Models in Continuous Time

Pierre-Louis Lions, Yves Achdou, Jean-Michel Lasry, 2013 .

Reinforcement Learning with Function Approximation: From Linear to Nonlinear

Jiequn Han, Jihao Long, 2023, Journal of Machine Learning.

Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games

Jiequn Han, Ruimeng Hu, 2019, MSML.

Solving optimal control of rigid-body dynamics with collisions using the hybrid minimum principle

E. Weinan, Weihua Hu, Jiequn Han, 2022, 2205.08622.

Improving Gradient Computation for Differentiable Physics Simulation with Contacts

Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, 2023, L4DC.

Uniformly accurate machine learning-based hydrodynamic models for kinetic equations

W. E, Chao Ma, Jiequn Han, 2019, Proceedings of the National Academy of Sciences.

Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning pdf

E Weinan, Arnulf Jentzen, Jiequn Han, 2017, ArXiv.

Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence

Jiequn Han, Ruimeng Hu, Jihao Long, 2022, SIAM J. Numer. Anal..

A class of dimension-free metrics for the convergence of empirical measures

Jiequn Han, Ruimeng Hu, Jihao Long, 2021, Stochastic Processes and their Applications.

Online Appendix: Numerical Methods for “Income and Wealth Distribution in Macroeconomics: A Continuous-Time Approach”

Benjamin Moll, J. Lasry, Y. Achdou, 2020 .

Learning Free Terminal Time Optimal Closed-loop Control of Manipulators

Jiequn Han, Jihao Long, E. Weinan, 2023, ArXiv.

Initial Value Problem Enhanced Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks

E. Weinan, Weihua Hu, Jiequn Han, 2022, 2209.04078.

Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm

Héctor D. Ceniceros, Jiequn Han, Ruimeng Hu, 2020, MSML.

Appendix : Numerical Methods for “ Income and Wealth Distribution in Macroeconomics : A Continuous-Time Approach ” 1

Benjamin Moll, J. Lasry, Y. Achdou, 2018 .