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2020 – today
- 2024
[j9]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): 1-24 (2024)- [i35]Joan Bruna, Jiequn Han:
Posterior Sampling with Denoising Oracles via Tilted Transport. CoRR abs/2407.00745 (2024) - [i34]Jiequn Han, Wei Hu, Jihao Long, Yue Zhao:
Deep Picard Iteration for High-Dimensional Nonlinear PDEs. CoRR abs/2409.08526 (2024) - 2023
- [j8]Jiequn Han, Xu-Hui Zhou, Heng Xiao:
An equivariant neural operator for developing nonlocal tensorial constitutive models. J. Comput. Phys. 488: 112243 (2023) - [j7]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: 112341 (2023) - [c7]Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis:
Improving Gradient Computation for Differentiable Physics Simulation with Contacts. L4DC 2023: 128-141 - [i33]Jihao Long, Jiequn Han:
Reinforcement Learning with Function Approximation: From Linear to Nonlinear. CoRR abs/2302.09703 (2023) - [i32]Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis:
Improving Gradient Computation for Differentiable Physics Simulation with Contacts. CoRR abs/2305.00092 (2023) - [i31]Wei Hu, Yue Zhao, Weinan E, Jiequn Han, Jihao Long:
Learning Free Terminal Time Optimal Closed-loop Control of Manipulators. CoRR abs/2311.17749 (2023) - [i30]Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen:
Stochastic Optimal Control Matching. CoRR abs/2312.02027 (2023) - 2022
- [j6]Zhong Li, Jiequn Han, Weinan E, Qianxiao Li:
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. J. Mach. Learn. Res. 23: 42:1-42:85 (2022) - [c6]Yaohua Zang, Jihao Long, Xuanxi Zhang, Wei Hu, Weinan E, Jiequn Han:
A Machine Learning Enhanced Algorithm for the Optimal Landing Problem. MSML 2022: 319-334 - [i29]Jiequn Han, Ruimeng Hu, Jihao Long:
Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence. CoRR abs/2204.11924 (2022) - [i28]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 abs/2207.05060 (2022) - [i27]Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Héctor D. Ceniceros:
Pandemic Control, Game Theory and Machine Learning. CoRR abs/2208.08646 (2022) - [i26]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 abs/2211.15930 (2022) - [i25]Mo Zhou, Jiequn Han, Manas Rachh, Carlos Borges:
A Neural Network Warm-Start Approach for the Inverse Acoustic Obstacle Scattering Problem. CoRR abs/2212.08736 (2022) - 2021
- [j5]Jiequn Han, Ruimeng Hu:
Recurrent neural networks for stochastic control problems with delay. Math. Control. Signals Syst. 33(4): 775-795 (2021) - [j4]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): A4043-A4066 (2021) - [c5]Zhong Li, Jiequn Han, Weinan E, Qianxiao Li:
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. ICLR 2021 - [c4]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: 10772-10782 - [c3]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: 987-1012 - [i24]Jiequn Han, Ruimeng Hu:
Recurrent Neural Networks for Stochastic Control Problems with Delay. CoRR abs/2101.01385 (2021) - [i23]Mo Zhou, Jiequn Han, Jianfeng Lu:
Actor-Critic Method for High Dimensional Static Hamilton-Jacobi-Bellman Partial Differential Equations based on Neural Networks. CoRR abs/2102.11379 (2021) - [i22]Xu-Hui Zhou, Jiequn Han, Heng Xiao:
Frame-independent vector-cloud neural network for nonlocal constitutive modelling on arbitrary grids. CoRR abs/2103.06685 (2021) - [i21]Jihao Long, Jiequn Han, Weinan E:
An L2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation. CoRR abs/2104.07794 (2021) - [i20]Jiequn Han, Ruimeng Hu, Jihao Long:
A Class of Dimensionality-free Metrics for the Convergence of Empirical Measures. CoRR abs/2104.12036 (2021) - [i19]Jihao Long, Jiequn Han:
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space. CoRR abs/2111.03469 (2021) - [i18]Jiequn Han, Yucheng Yang, Weinan E:
DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks. CoRR abs/2112.14377 (2021) - [i17]Muhammad I. Zafar, Jiequn Han, Xu-Hui Zhou, Heng Xiao:
Frame invariance and scalability of neural operators for partial differential equations. CoRR abs/2112.14769 (2021) - 2020
- [j3]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: 109792 (2020) - [c2]Jiequn Han, Ruimeng Hu:
Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games. MSML 2020: 221-245 - [i16]Jiequn Han, Jianfeng Lu, Mo Zhou:
Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach. CoRR abs/2002.02600 (2020) - [i15]Xin Guo, Jiequn Han, Wenpin Tang:
Perturbed gradient descent with occupation time. CoRR abs/2005.04507 (2020) - [i14]Weinan E, Jiequn Han, Linfeng Zhang:
Integrating Machine Learning with Physics-Based Modeling. CoRR abs/2006.02619 (2020) - [i13]Jiequn Han, Ruimeng Hu, Jihao Long:
Convergence of Deep Fictitious Play for Stochastic Differential Games. CoRR abs/2008.05519 (2020) - [i12]Weinan E, Jiequn Han, Arnulf Jentzen:
Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning. CoRR abs/2008.13333 (2020) - [i11]Zhong Li, Jiequn Han, Weinan E, Qianxiao Li:
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. CoRR abs/2009.07799 (2020) - [i10]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 abs/2012.06745 (2020)
2010 – 2019
- 2019
- [j2]Jiequn Han, Linfeng Zhang, Weinan E:
Solving many-electron Schrödinger equation using deep neural networks. J. Comput. Phys. 399 (2019) - [i9]Jiequn Han, Yingzhou Li, Lin Lin, Jianfeng Lu, Jiefu Zhang, Linfeng Zhang:
Universal approximation of symmetric and anti-symmetric functions. CoRR abs/1912.01765 (2019) - [i8]Jiequn Han, Ruimeng Hu:
Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games. CoRR abs/1912.01809 (2019) - 2018
- [j1]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: 178-184 (2018) - [c1]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: 4441-4451 - [i7]Weinan E, Jiequn Han, Qianxiao Li:
A Mean-Field Optimal Control Formulation of Deep Learning. CoRR abs/1807.01083 (2018) - [i6]Jiequn Han, Jihao Long:
Convergence of the Deep BSDE Method for Coupled FBSDEs. CoRR abs/1811.01165 (2018) - 2017
- [i5]Weinan E, Jiequn Han, Arnulf Jentzen:
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. CoRR abs/1706.04702 (2017) - [i4]Jiequn Han, Arnulf Jentzen, Weinan E:
Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning. CoRR abs/1707.02568 (2017) - [i3]Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E:
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics. CoRR abs/1707.09571 (2017) - [i2]Han Wang, Linfeng Zhang, Jiequn Han, Weinan E:
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. CoRR abs/1712.03641 (2017) - 2016
- [i1]Jiequn Han, Weinan E:
Deep Learning Approximation for Stochastic Control Problems. CoRR abs/1611.07422 (2016)
Coauthor Index
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