1 code implementation • 4 Dec 2023 • Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen

Our work introduces Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control that stems from the same philosophy as the conditional score matching loss for diffusion models.

1 code implementation • 28 Apr 2023 • Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis

We find that existing differentiable simulation methods provide inaccurate gradients when the contact normal direction is not fixed - a general situation when the contacts are between two moving objects.

no code implementations • 20 Feb 2023 • Jihao Long, Jiequn Han

These results rely on the $L^\infty$ and UCB estimation of estimation error, which can handle the distribution mismatch phenomenon.

1 code implementation • 16 Dec 2022 • Mo Zhou, Jiequn Han, Manas Rachh, Carlos Borges

We present a neural network warm-start approach for solving the inverse scattering problem, where an initial guess for the optimization problem is obtained using a trained neural network.

1 code implementation • 29 Nov 2022 • Yue Zhao, Jiequn Han

We first conduct a comparative study of two prevalent approaches: offline supervised learning and online direct policy optimization.

no code implementations • 18 Aug 2022 • Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Hector D. Ceniceros

Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels.

1 code implementation • 8 Jul 2022 • Yaofeng Desmond Zhong, Jiequn Han, Georgia Olympia Brikis

In recent years, an increasing amount of work has focused on differentiable physics simulation and has produced a set of open source projects such as Tiny Differentiable Simulator, Nimble Physics, diffTaichi, Brax, Warp, Dojo and DiffCoSim.

1 code implementation • 25 Apr 2022 • Jiequn Han, Ruimeng Hu, Jihao Long

These coefficient functions are used to approximate the MV-FBSDEs' model coefficients with full distribution dependence, and are updated by solving another supervising learning problem using training data simulated from the last iteration's FBSDE solutions.

no code implementations • 29 Dec 2021 • Jiequn Han, Yucheng Yang, Weinan E

An efficient, reliable, and interpretable global solution method, the Deep learning-based algorithm for Heterogeneous Agent Models (DeepHAM), is proposed for solving high dimensional heterogeneous agent models with aggregate shocks.

no code implementations • 28 Dec 2021 • Muhammad I. Zafar, Jiequn Han, Xu-Hui Zhou, Heng Xiao

Partial differential equations (PDEs) play a dominant role in the mathematical modeling of many complex dynamical processes.

no code implementations • 5 Nov 2021 • Jihao Long, Jiequn Han

As a byproduct, we show that when the reward functions lie in a high dimensional RKHS, even if the transition probability is known and the action space is finite, it is still possible for RL problems to suffer from the curse of dimensionality.

no code implementations • 24 Apr 2021 • Jiequn Han, Ruimeng Hu, Jihao Long

The proposed metrics fall into the category of integral probability metrics, for which we specify criteria of test function spaces to guarantee the property of being free of CoD.

no code implementations • 15 Apr 2021 • Jihao Long, Jiequn Han, Weinan E

Reinforcement learning (RL) algorithms based on high-dimensional function approximation have achieved tremendous empirical success in large-scale problems with an enormous number of states.

2 code implementations • 11 Mar 2021 • Xu-Hui Zhou, Jiequn Han, Heng Xiao

As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations.

1 code implementation • 5 Jan 2021 • Jiequn Han, Ruimeng Hu

Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions.

no code implementations • 12 Dec 2020 • Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Hector D. Ceniceros

Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels.

no code implementations • ICLR 2021 • Zhong Li, Jiequn Han, Weinan E, Qianxiao Li

We study the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn input-output relationships in temporal data.

no code implementations • 16 Aug 2020 • Weichen Wang, Jiequn Han, Zhuoran Yang, Zhaoran Wang

Reinforcement learning is a powerful tool to learn the optimal policy of possibly multiple agents by interacting with the environment.

no code implementations • 12 Aug 2020 • Jiequn Han, Ruimeng Hu, Jihao Long

Stochastic differential games have been used extensively to model agents' competitions in Finance, for instance, in P2P lending platforms from the Fintech industry, the banking system for systemic risk, and insurance markets.

no code implementations • 4 Jun 2020 • Weinan E, Jiequn Han, Linfeng Zhang

Machine learning is poised as a very powerful tool that can drastically improve our ability to carry out scientific research.

no code implementations • 9 May 2020 • Xin Guo, Jiequn Han, Mahan Tajrobehkar, Wenpin Tang

Motivated by the super-diffusivity of self-repelling random walk, which has roots in statistical physics, this paper develops a new perturbation mechanism for optimization algorithms.

no code implementations • 7 Feb 2020 • Jiequn Han, Jianfeng Lu, Mo Zhou

We propose a new method to solve eigenvalue problems for linear and semilinear second order differential operators in high dimensions based on deep neural networks.

no code implementations • 4 Dec 2019 • Jiequn Han, Ruimeng Hu

We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games.

no code implementations • 3 Nov 2018 • Jiequn Han, Jihao Long

The recently proposed numerical algorithm, deep BSDE method, has shown remarkable performance in solving high-dimensional forward-backward stochastic differential equations (FBSDEs) and parabolic partial differential equations (PDEs).

no code implementations • 18 Jul 2018 • Jiequn Han, Linfeng Zhang, Weinan E

We introduce a new family of trial wave-functions based on deep neural networks to solve the many-electron Schr\"odinger equation.

Computational Physics Chemical Physics

no code implementations • 3 Jul 2018 • Weinan E, Jiequn Han, Qianxiao Li

This paper introduces the mathematical formulation of the population risk minimization problem in deep learning as a mean-field optimal control problem.

1 code implementation • NeurIPS 2018 • Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, Weinan E

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology.

Computational Physics Materials Science Chemical Physics

2 code implementations • 11 Dec 2017 • Han Wang, Linfeng Zhang, Jiequn Han, Weinan E

Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics.

5 code implementations • 30 Jul 2017 • Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E

We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data.

6 code implementations • 9 Jul 2017 • Jiequn Han, Arnulf Jentzen, Weinan E

Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the "curse of dimensionality".

1 code implementation • 5 Jul 2017 • Jiequn Han, Linfeng Zhang, Roberto Car, Weinan E

When tested on a wide variety of examples, Deep Potential is able to reproduce the original model, whether empirical or quantum mechanics based, within chemical accuracy.

Computational Physics

5 code implementations • 15 Jun 2017 • Weinan E, Jiequn Han, Arnulf Jentzen

We propose a new algorithm for solving parabolic partial differential equations (PDEs) and backward stochastic differential equations (BSDEs) in high dimension, by making an analogy between the BSDE and reinforcement learning with the gradient of the solution playing the role of the policy function, and the loss function given by the error between the prescribed terminal condition and the solution of the BSDE.

no code implementations • 2 Nov 2016 • Jiequn Han, Weinan E

Many real world stochastic control problems suffer from the "curse of dimensionality".

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