Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Han, Jiequn"'
Autor:
Bruna, Joan, Han, Jiequn
Score-based diffusion models have significantly advanced high-dimensional data generation across various domains, by learning a denoising oracle (or score) from datasets. From a Bayesian perspective, they offer a realistic modeling of data priors and
Externí odkaz:
http://arxiv.org/abs/2407.00745
In recent years, using neural networks to speed up the solving of partial differential equations (PDEs) has gained significant traction in both academic and industrial settings. However, the use of neural networks as standalone surrogate models raise
Externí odkaz:
http://arxiv.org/abs/2312.11842
Stochastic optimal control, which has the goal of driving the behavior of noisy systems, is broadly applicable in science, engineering and artificial intelligence. Our work introduces Stochastic Optimal Control Matching (SOCM), a novel Iterative Diff
Externí odkaz:
http://arxiv.org/abs/2312.02027
Publikováno v:
Proceedings of Machine Learning Research vol 211, 2023
Differentiable simulation enables gradients to be back-propagated through physics simulations. In this way, one can learn the dynamics and properties of a physics system by gradient-based optimization or embed the whole differentiable simulation as a
Externí odkaz:
http://arxiv.org/abs/2305.00092
In many scientific and engineering problems, noise and nonlinearity are unavoidable, which could induce interesting mathematical problem such as transition phenomena. This paper focuses on efficiently discovering the most probable transition pathway
Externí odkaz:
http://arxiv.org/abs/2304.12994
Autor:
Zeng, Jinzhe, Zhang, Duo, Lu, Denghui, Mo, Pinghui, Li, Zeyu, Chen, Yixiao, Rynik, Marián, Huang, Li'ang, Li, Ziyao, Shi, Shaochen, Wang, Yingze, Ye, Haotian, Tuo, Ping, Yang, Jiabin, Ding, Ye, Li, Yifan, Tisi, Davide, Zeng, Qiyu, Bao, Han, Xia, Yu, Huang, Jiameng, Muraoka, Koki, Wang, Yibo, Chang, Junhan, Yuan, Fengbo, Bore, Sigbjørn Løland, Cai, Chun, Lin, Yinnian, Wang, Bo, Xu, Jiayan, Zhu, Jia-Xin, Luo, Chenxing, Zhang, Yuzhi, Goodall, Rhys E. A., Liang, Wenshuo, Singh, Anurag Kumar, Yao, Sikai, Zhang, Jingchao, Wentzcovitch, Renata, Han, Jiequn, Liu, Jie, Jia, Weile, York, Darrin M., E, Weinan, Car, Roberto, Zhang, Linfeng, Wang, Han
Publikováno v:
J. Chem. Phys. 159, 054801 (2023)
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the f
Externí odkaz:
http://arxiv.org/abs/2304.09409
Autor:
Long, Jihao, Han, Jiequn
Publikováno v:
J. Mach. Learn. , 2 (2023), pp. 161-193
Function approximation has been an indispensable component in modern reinforcement learning algorithms designed to tackle problems with large state spaces in high dimensions. This paper reviews recent results on error analysis for these reinforcement
Externí odkaz:
http://arxiv.org/abs/2302.09703
Publikováno v:
Journal of Computational Physics, 488, 112243 (2023)
We consider the inverse acoustic obstacle problem for sound-soft star-shaped obstacles in two dimensions wherein the boundary of the obstacle is determined from measurements of the scattered field at a collection of receivers outside the object. One
Externí odkaz:
http://arxiv.org/abs/2212.08736
Autor:
Zhao, Yue, Han, Jiequn
This work is concerned with solving neural network-based feedback controllers efficiently for optimal control problems. We first conduct a comparative study of two prevalent approaches: offline supervised learning and online direct policy optimizatio
Externí odkaz:
http://arxiv.org/abs/2211.15930
Closed-loop optimal control design for high-dimensional nonlinear systems has been a long-standing challenge. Traditional methods, such as solving the associated Hamilton-Jacobi-Bellman equation, suffer from the curse of dimensionality. Recent litera
Externí odkaz:
http://arxiv.org/abs/2209.04078