Deep Learning Approximation for Stochastic Control Problems. - Jiequn Han; Weinan E (CoRR, 2016)

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. - Weinan E; Jiequn Han; Arnulf Jentzen (CoRR, 2017)

Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning. - Jiequn Han; Arnulf Jentzen; Weinan E (CoRR, 2017)

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics. - Linfeng Zhang 002; Jiequn Han; Han Wang 006; Roberto Car; Weinan E (CoRR, 2017)

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. - Linfeng Zhang 002; Jiequn Han; Han Wang 006; Wissam Saidi; Roberto Car; Weinan E (NeurIPS, 2018)

A Mean-Field Optimal Control Formulation of Deep Learning. - Weinan E; Jiequn Han; Qianxiao Li (CoRR, 2018)

Convergence of the Deep BSDE Method for Coupled FBSDEs. - Jiequn Han; Jihao Long (CoRR, 2018)

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. - Han Wang 006; Linfeng Zhang 002; Jiequn Han; Weinan E (Comput. Phys. Commun., 2018)

Universal approximation of symmetric and anti-symmetric functions. - Jiequn Han; Yingzhou Li; Lin Lin 001; Jianfeng Lu 001; Jiefu Zhang; Linfeng Zhang 002 (CoRR, 2019)

Solving many-electron Schrödinger equation using deep neural networks. - Jiequn Han; Linfeng Zhang 002; Weinan E (J. Comput. Phys., 2019)

Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games. - Jiequn Han; Ruimeng Hu (MSML, 2020)

Perturbed gradient descent with occupation time. - Xin Guo; Jiequn Han; Wenpin Tang (CoRR, 2020)

Integrating Machine Learning with Physics-Based Modeling. - Weinan E; Jiequn Han; Linfeng Zhang 002 (CoRR, 2020)

Convergence of Deep Fictitious Play for Stochastic Differential Games. - Jiequn Han; Ruimeng Hu; Jihao Long (CoRR, 2020)

Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning. - Weinan E; Jiequn Han; Arnulf Jentzen (CoRR, 2020)

Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach. - Jiequn Han; Jianfeng Lu 001; Mo Zhou (J. Comput. Phys., 2020)

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. - Zhong Li 004; Jiequn Han; Weinan E; Qianxiao Li (ICLR, 2021)

Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time. - Weichen Wang; Jiequn Han; Zhuoran Yang; Zhaoran Wang 001 (ICML, 2021)

Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm. - Yao Xuan; Robert Balkin; Jiequn Han; Ruimeng Hu; Héctor D. Ceniceros (MSML, 2021)

Frame-independent vector-cloud neural network for nonlocal constitutive modelling on arbitrary grids. - Xu-Hui Zhou; Jiequn Han; Heng Xiao (CoRR, 2021)

An L2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation. - Jihao Long; Jiequn Han; Weinan E (CoRR, 2021)

A Class of Dimensionality-free Metrics for the Convergence of Empirical Measures. - Jiequn Han; Ruimeng Hu; Jihao Long (CoRR, 2021)

Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space. - Jihao Long; Jiequn Han (CoRR, 2021)

DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks. - Jiequn Han; Yucheng Yang; Weinan E (CoRR, 2021)

Frame invariance and scalability of neural operators for partial differential equations. - Muhammad I. Zafar; Jiequn Han; Xu-Hui Zhou; Heng Xiao (CoRR, 2021)

Recurrent neural networks for stochastic control problems with delay. - Jiequn Han; Ruimeng Hu (Math. Control. Signals Syst., 2021)

Actor-Critic Method for High Dimensional Static Hamilton-Jacobi-Bellman Partial Differential Equations based on Neural Networks. - Mo Zhou; Jiequn Han; Jianfeng Lu 001 (SIAM J. Sci. Comput., 2021)

A Machine Learning Enhanced Algorithm for the Optimal Landing Problem. - Yaohua Zang; Jihao Long; Xuanxi Zhang; Wei Hu; Weinan E; Jiequn Han (MSML, 2022)

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 (CoRR, 2022)

Pandemic Control, Game Theory and Machine Learning. - Yao Xuan; Robert Balkin; Jiequn Han; Ruimeng Hu; Héctor D. Ceniceros (CoRR, 2022)

Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control. - Yue Zhao; Jiequn Han (CoRR, 2022)

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. - Zhong Li 004; Jiequn Han; Weinan E; Qianxiao Li (J. Mach. Learn. Res., 2022)

Improving Gradient Computation for Differentiable Physics Simulation with Contacts. - Yaofeng Desmond Zhong; Jiequn Han; Biswadip Dey; Georgia Olympia Brikis (L4DC, 2023)

Reinforcement Learning with Function Approximation: From Linear to Nonlinear. - Jihao Long; Jiequn Han (CoRR, 2023)

A neural network warm-start approach for the inverse acoustic obstacle scattering problem. - Mo Zhou; Jiequn Han; Manas Rachh; Carlos Borges (J. Comput. Phys., 2023)

An equivariant neural operator for developing nonlocal tensorial constitutive models. - Jiequn Han; Xu-Hui Zhou; Heng Xiao (J. Comput. Phys., 2023)

Provable Posterior Sampling with Denoising Oracles via Tilted Transport. - Joan Bruna; Jiequn Han (NeurIPS, 2024)

Stochastic Optimal Control Matching. - Carles Domingo-Enrich; Jiequn Han; Brandon Amos; Joan Bruna; Ricky T. Q. Chen (NeurIPS, 2024)

Posterior Sampling with Denoising Oracles via Tilted Transport. - Joan Bruna; Jiequn Han (CoRR, 2024)

Deep Picard Iteration for High-Dimensional Nonlinear PDEs. - Jiequn Han; Wei Hu; Jihao Long; Yue Zhao (CoRR, 2024)

Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence. - Jiequn Han; Ruimeng Hu; Jihao Long (SIAM J. Numer. Anal., 2024)

Learning Free Terminal Time Optimal Closed-loop Control of Manipulators. - Wei Hu; Yue Zhao; Weinan E; Jiequn Han; Jihao Long (ACC, 2025)

Solving optimal control problems of rigid-body dynamics with collisions using the hybrid minimum principle. - Wei Hu; Jihao Long; Yaohua Zang; Weinan E; Jiequn Han (Commun. Nonlinear Sci. Numer. Simul., 2025)

Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulations. - Xu-Hui Zhou; Jiequn Han; Muhammad I. Zafar; Eric M. Wolf; Christopher R. Schrock; Christopher J. Roy; Heng Xiao (J. Comput. Phys., 2025)

DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction. - Rudy Morel; Jiequn Han; Edouard Oyallon (CoRR, 2025)

A brief review of the Deep BSDE method for solving high-dimensional partial differential equations. - Jiequn Han; Arnulf Jentzen; Weinan E (CoRR, 2025)