Jiequn Han
Orcid: 0000-0002-3553-7313
According to our database1,
Jiequn Han
authored at least 39 papers
between 2016 and 2024.
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Bibliography
2024
Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence.
SIAM J. Numer. Anal., February, 2024
2023
An equivariant neural operator for developing nonlocal tensorial constitutive models.
J. Comput. Phys., September, 2023
A neural network warm-start approach for the inverse acoustic obstacle scattering problem.
J. Comput. Phys., 2023
CoRR, 2023
Proceedings of the Learning for Dynamics and Control Conference, 2023
2022
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks.
J. Mach. Learn. Res., 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.
CoRR, 2022
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?
CoRR, 2022
Proceedings of the Mathematical and Scientific Machine Learning, 2022
2021
Actor-Critic Method for High Dimensional Static Hamilton-Jacobi-Bellman Partial Differential Equations based on Neural Networks.
SIAM J. Sci. Comput., 2021
Math. Control. Signals Syst., 2021
Frame invariance and scalability of neural operators for partial differential equations.
CoRR, 2021
DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks.
CoRR, 2021
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space.
CoRR, 2021
CoRR, 2021
An L<sup>2</sup> Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation.
CoRR, 2021
Frame-independent vector-cloud neural network for nonlocal constitutive modelling on arbitrary grids.
CoRR, 2021
Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm.
Proceedings of the Mathematical and Scientific Machine Learning, 2021
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time.
Proceedings of the 38th International Conference on Machine Learning, 2021
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis.
Proceedings of the 9th International Conference on Learning Representations, 2021
2020
Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach.
J. Comput. Phys., 2020
Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning.
CoRR, 2020
Proceedings of Mathematical and Scientific Machine Learning, 2020
2019
J. Comput. Phys., 2019
2018
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.
Comput. Phys. Commun., 2018
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics.
CoRR, 2017
Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning.
CoRR, 2017
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations.
CoRR, 2017
2016