Zobrazeno 1 - 10
of 4 188
pro vyhledávání: '"Bao, Feng"'
Autor:
Harris, Sumner B., Fajardo, Ruth, Puretzky, Alexander A., Xiao, Kai, Bao, Feng, Vasudevan, Rama K.
The rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable the re
Externí odkaz:
http://arxiv.org/abs/2410.23895
In this work, we present a numerical method that provides accurate real-time detection for the widths of the fractures in a fractured porous medium based on observational data on porous medium fluid mass and velocity. To achieve this task, an inverse
Externí odkaz:
http://arxiv.org/abs/2409.16458
Autor:
Yin, Junqi, Liang, Siming, Liu, Siyan, Bao, Feng, Chipilski, Hristo G., Lu, Dan, Zhang, Guannan
The weather and climate domains are undergoing a significant transformation thanks to advances in AI-based foundation models such as FourCastNet, GraphCast, ClimaX and Pangu-Weather. While these models show considerable potential, they are not ready
Externí odkaz:
http://arxiv.org/abs/2407.12168
Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm
This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component
Externí odkaz:
http://arxiv.org/abs/2405.20182
Autor:
Lyu, Yunzheng, Bao, Feng
Kernel learning forward backward SDE filter is an iterative and adaptive meshfree approach to solve the nonlinear filtering problem. It builds from forward backward SDE for Fokker-Planker equation, which defines evolving density for the state variabl
Externí odkaz:
http://arxiv.org/abs/2405.13390
Autor:
Wang, Fei, Lu, Kannan, Zhan, Huijuan, Ma, Lu, Wu, Feng, Sun, Hantao, Deng, Hao, Bai, Yang, Bao, Feng, Chang, Xu, Gao, Ran, Gao, Xun, Gong, Guicheng, Hu, Lijuan, Hu, Ruizi, Ji, Honghong, Ma, Xizheng, Mao, Liyong, Song, Zhijun, Tang, Chengchun, Wang, Hongcheng, Wang, Tenghui, Wang, Ziang, Xia, Tian, Xu, Hongxin, Zhan, Ze, Zhang, Gengyan, Zhou, Tao, Zhu, Mengyu, Zhu, Qingbin, Zhu, Shasha, Zhu, Xing, Shi, Yaoyun, Zhao, Hui-Hai, Deng, Chunqing
Fluxonium qubits are recognized for their high coherence times and high operation fidelities, attributed to their unique design incorporating over 100 Josephson junctions per superconducting loop. However, this complexity poses significant fabricatio
Externí odkaz:
http://arxiv.org/abs/2405.05481
Publikováno v:
SIGMA 20 (2024), 091, 14 pages
In the present paper, an integrable semi-discretization of the modified Camassa-Holm (mCH) equation with cubic nonlinearity is presented. The key points of the construction are based on the discrete Kadomtsev-Petviashvili (KP) equation and appropriat
Externí odkaz:
http://arxiv.org/abs/2404.18372
The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here we explore another promising solution in which diffusion models are used to formulate a robust
Externí odkaz:
http://arxiv.org/abs/2404.00844
This paper presents a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assimilation techniques and optimal control solvers to manage partial obse
Externí odkaz:
http://arxiv.org/abs/2404.05734
We report two methods for solving FBSDEs of path dependent types of high dimensions. Specifically, we propose a deep learning framework for solving such problems using path signatures as underlying features. Our two methods (forward/backward) demonst
Externí odkaz:
http://arxiv.org/abs/2402.06042