Search

Your search keyword '"Zhang, Zhenyi"' showing total 647 results
Start Over Author "Zhang, Zhenyi"
647 results on '"Zhang, Zhenyi"'

1. Learning Stochastic Dynamics from Snapshots through Regularized Unbalanced Optimal Transport

Author

Zhang, Zhenyi, Li, Tiejun, and Zhou, Peijie

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Physics - Computational Physics, Quantitative Biology - Quantitative Methods
Abstract

Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning. Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots. Based on the RUOT form, our method models these dynamics without requiring prior knowledge of growth and death processes or additional information, allowing them to be learnt directly from data. Theoretically, we explore the connections between the RUOT and Schr\"odinger bridge problem and discuss the key challenges and potential solutions. The effectiveness of our method is demonstrated with a synthetic gene regulatory network. Compared with other methods, our approach accurately identifies growth and transition patterns, eliminates false transitions, and constructs the Waddington developmental landscape.

Published
2024

2. Uncertainty Quantification of Phase Transition Problems with an Injection Boundary

Author

Zhang, Zhenyi, Ma, Shengbo, and Zhou, Zhennan

Subjects
Mathematics - Numerical Analysis
Abstract

We develop an enthalpy-based modeling and computational framework to quantify uncertainty in Stefan problems with an injection boundary. Inspired by airfoil icing studies, we consider a system featuring an injection boundary inducing domain changes and a free boundary separating phases, resulting in two types of moving boundaries. Our proposed enthalpy-based formulation seamlessly integrates thermal diffusion across the domain with energy fluxes at the boundaries, addressing a modified injection condition for boundary movement. Uncertainty then stems from random variations in the injection boundary. The primary focus of our Uncertainty Quantification (UQ) centers on investigating the effects of uncertainty on free boundary propagation. Through mapping to a reference domain, we derive an enthalpy-based numerical scheme tailored to the transformed coordinate system, facilitating a simple and efficient simulation. Numerical and UQ studies in one and two dimensions validate the proposed model and the extended enthalpy method. They offer intriguing insights into ice accretion and other multiphysics processes involving phase transitions.

Published
2024

6. Experimental and Numerical Simulation Study of Dynamic Mechanical Behavior of AlMgB14 Ceramic

Author

Zhang, Zhenyi, Xia, Fangchao, Cao, Zhongchen, Yuan, Fei, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Rui, Xiaoting, editor, and Liu, Caishan, editor

Published
2024
Full Text
View/download PDF

12. Effects of electron-beam irradiation on the control of bacterium and insect pests in warehoused tobacco and on tobacco quality

Author

WANG Kun, ZHANG Qi, XU Fengzhuo, LI Haozhe, CHEN Xihao, CHEN Zhiyan, ZHOU Zijian, ZHANG Zhenyi, and HUANG Jiang

Subjects
tobacco storage, irradiation sterilization, irradiation insecticide, lasioderma serricorne, tobacco quality, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

The effects of 200 keV and 10 MeV electron-irradiation accelerators on the eradication of lasioderma serricorne and bacteria in tobacco and on the quality of tobacco leaf are investigated. By controlling the parameters of electron-beam irradiation, all lasioderma serricorne and bacteria inside tobacco can be eradicated efficiently and rapidly. The irradiated tobacco exhibits good retention based on a validation experiment, and no colonies are detected within 38 d. The irradiation has no significant effects on the basic physical and chemical indexes or the organic-acid and mineral-element contents of tobacco. Excessive energy levels reduce the volatilization loss of neophytadiene in tobacco from 589.91 to 418.64 μg/g, which indicates that the aroma quality of the tobacco deteriorates to a certain extent. Fluka simulation results show that a 3 MeV electron beam is sufficient to penetrate a full box of stored tobacco at existing tobacco stacking densities.

Published
2024
Full Text
View/download PDF

26. A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination Systems

Author

Mo, Zhibin, Zhang, Zhenyi, Chen, Yutao, Huang, Jie, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, Fu, Yongling, editor, Yu, Zhiyuan, editor, and Zheng, Song, editor

Published
2022
Full Text
View/download PDF

45. Chiral Separation of Copper Sulfide [S–Cu36] Nanocluster Using a Chiral Adaptive Counterion

Author

Guo, Yumeng, Zhang, Zhenyi, Han, Haixiang, and Zhou, Zheng

Abstract

This work presents a new strategy to achieve the growth of copper sulfide nanoclusters with high nuclearity. Through a phosphine-assisted C–S reductive cleavage approach, an intrinsically chiral [Cu4] cluster passes through a [S–Cu9] cluster and transforms into a higher-nuclearity [S–Cu36] cluster, which features a core–shell structure with a [Cu4]4+core encapsulated by a chiral [Cu20S12] shell. Interestingly, the spiral arrangement of the bidental ligands on the surface of the [S–Cu36] cluster leads to the L-/R-enantiomeric configurations. Moreover, by utilization of [Na(THF)6]+as a chiral adaptive counterion, [S–Cu36] can be interlocked separately, thus enabling the isolation of homochiral clusters. Theoretical calculation suggests that the configuration transition between two enantiomeric [Na(THF)6]+species is favorable at room temperature, thereby promoting the cocrystallization of resulting chiral products. This study introduces a novel perspective on the synthesis of chiral copper sulfide nanoclusters and presents an innovative approach to achieving the chiral separation of nanoclusters.

Published
2024
Full Text
View/download PDF

50. Research on Cargo Volume Prediction and Adjustment Strategy of Logistics Network Based on Deep Learning and Optimisation Algorithm.

Author

Li, Haoxiang, Yu, Yangcheng, and Zhang, Zhenyi

Subjects
PARTICLE swarm optimization, SIMULATED annealing, OPTIMIZATION algorithms, MACHINE learning, LINEAR programming, DEEP learning
Abstract

Despite existing studies discussing logistics volume forecasting and logistics network optimization, dynamically adjusting the logistics network structure and evaluating the importance of logistics sites and routes during emergencies remains challenging. Consequently, this study aims to develop a line volume prediction model to forecast the volume at each logistics site and route in the future. Building on this, a mathematical model is constructed to determine how to adjust the logistics network when a logistics site is deactivated, thereby reducing operational costs and enhancing efficiency. This paper tackles the issues of logistics site and route volume prediction, allocation, and network restructuring by developing prediction and optimization models. Initially, we utilize the Long Short-Term Memory (LSTM) neural network to create a route cargo volume prediction model, forecasting the route cargo volume for the upcoming month and providing prediction results for three routes: DC14→DC10, DC20→DC35, and DC25→DC62. Subsequently, a linear programming model based on the simulated annealing algorithm is formulated to address the cargo volume allocation following the closure of logistics site DC5. Finally, we establish a logistics network optimization model using the particle swarm optimization algorithm to dynamically adjust the logistics network structure. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results