Jiequn Han

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 50

Top Topics

High Dimensional

Partial Differential Equations

Top Venues

J. Comput. Phys.

Publications

Jiequn Han, Ruimeng Hu, Jihao Long
Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence. SIAM J. Numer. Anal. 62 (1) (2024)
Joan Bruna, Jiequn Han
Posterior Sampling with Denoising Oracles via Tilted Transport. CoRR (2024)
Jihao Long, Jiequn Han
Reinforcement Learning with Function Approximation: From Linear to Nonlinear. CoRR (2023)
Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis
Improving Gradient Computation for Differentiable Physics Simulation with Contacts. L4DC (2023)
Jiequn Han, Xu-Hui Zhou, Heng Xiao
An equivariant neural operator for developing nonlocal tensorial constitutive models. J. Comput. Phys. 488 (2023)
Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen
Stochastic Optimal Control Matching. CoRR (2023)
Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis
Improving Gradient Computation for Differentiable Physics Simulation with Contacts. CoRR (2023)
Wei Hu, Yue Zhao, Weinan E, Jiequn Han, Jihao Long
Learning Free Terminal Time Optimal Closed-loop Control of Manipulators. CoRR (2023)
Mo Zhou, Jiequn Han, Manas Rachh, Carlos Borges
A neural network warm-start approach for the inverse acoustic obstacle scattering problem. J. Comput. Phys. 490 (2023)
Mo Zhou, Jiequn Han, Manas Rachh, Carlos Borges
A Neural Network Warm-Start Approach for the Inverse Acoustic Obstacle Scattering Problem. CoRR (2022)
Yue Zhao, Jiequn Han
Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control. CoRR (2022)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. J. Mach. Learn. Res. 23 (2022)
Jiequn Han, Ruimeng Hu, Jihao Long
Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence. CoRR (2022)
Yaohua Zang, Jihao Long, Xuanxi Zhang, Wei Hu, Weinan E, Jiequn Han
A Machine Learning Enhanced Algorithm for the Optimal Landing Problem. MSML (2022)
Yaofeng Desmond Zhong, Jiequn Han, Georgia Olympia Brikis
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control? CoRR (2022)
Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Héctor D. Ceniceros
Pandemic Control, Game Theory and Machine Learning. CoRR (2022)
Mo Zhou, Jiequn Han, Jianfeng Lu
Actor-Critic Method for High Dimensional Static Hamilton-Jacobi-Bellman Partial Differential Equations based on Neural Networks. SIAM J. Sci. Comput. 43 (6) (2021)
Jiequn Han, Ruimeng Hu
Recurrent neural networks for stochastic control problems with delay. Math. Control. Signals Syst. 33 (4) (2021)
Weichen Wang, Jiequn Han, Zhuoran Yang, Zhaoran Wang
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time. ICML (2021)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. ICLR (2021)
Jihao Long, Jiequn Han
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space. CoRR (2021)
Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Héctor D. Ceniceros
Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm. MSML (2021)
Jihao Long, Jiequn Han, Weinan E
Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation. CoRR (2021)
Jiequn Han, Ruimeng Hu, Jihao Long
A Class of Dimensionality-free Metrics for the Convergence of Empirical Measures. CoRR (2021)
Muhammad I. Zafar, Jiequn Han, Xu-Hui Zhou, Heng Xiao
Frame invariance and scalability of neural operators for partial differential equations. CoRR (2021)
Jiequn Han, Ruimeng Hu
Recurrent Neural Networks for Stochastic Control Problems with Delay. CoRR (2021)
Mo Zhou, Jiequn Han, Jianfeng Lu
Actor-Critic Method for High Dimensional Static Hamilton-Jacobi-Bellman Partial Differential Equations based on Neural Networks. CoRR (2021)
Jiequn Han, Yucheng Yang, Weinan E
DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks. CoRR (2021)
Xu-Hui Zhou, Jiequn Han, Heng Xiao
Frame-independent vector-cloud neural network for nonlocal constitutive modelling on arbitrary grids. CoRR (2021)
Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Héctor D. Ceniceros
Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm. CoRR (2020)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. CoRR (2020)
Jiequn Han, Jianfeng Lu, Mo Zhou
Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach. J. Comput. Phys. 423 (2020)
Jiequn Han, Jianfeng Lu, Mo Zhou
Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach. CoRR (2020)
Weinan E, Jiequn Han, Arnulf Jentzen
Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning. CoRR (2020)
Xin Guo, Jiequn Han, Wenpin Tang
Perturbed gradient descent with occupation time. CoRR (2020)
Jiequn Han, Ruimeng Hu, Jihao Long
Convergence of Deep Fictitious Play for Stochastic Differential Games. CoRR (2020)
Weinan E, Jiequn Han, Linfeng Zhang
Integrating Machine Learning with Physics-Based Modeling. CoRR (2020)
Jiequn Han, Ruimeng Hu
Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games. MSML (2020)
Jiequn Han, Yingzhou Li, Lin Lin, Jianfeng Lu, Jiefu Zhang, Linfeng Zhang
Universal approximation of symmetric and anti-symmetric functions. CoRR (2019)
Jiequn Han, Ruimeng Hu
Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games. CoRR (2019)
Jiequn Han, Linfeng Zhang, Weinan E
Solving many-electron Schrödinger equation using deep neural networks. J. Comput. Phys. 399 (2019)
Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228 (2018)
Weinan E, Jiequn Han, Qianxiao Li
A Mean-Field Optimal Control Formulation of Deep Learning. CoRR (2018)
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. NeurIPS (2018)
Jiequn Han, Jihao Long
Convergence of the Deep BSDE Method for Coupled FBSDEs. CoRR (2018)
Jiequn Han, Arnulf Jentzen, Weinan E
Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning. CoRR (2017)
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics. CoRR (2017)
Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. CoRR (2017)
Weinan E, Jiequn Han, Arnulf Jentzen
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. CoRR (2017)
Jiequn Han, Weinan E
Deep Learning Approximation for Stochastic Control Problems. CoRR (2016)