The calibration for many-body dissipative particle dynamics by using back-propagation neural networks-Reference-Cited by-同舟云学术

The calibration for many-body dissipative particle dynamics by using back-propagation neural networks

Published:2022-03-29 Issue:11 Volume:48 Page:955-964
ISSN:0892-7022
Container-title:Molecular Simulation
language:en
Short-container-title:Molecular Simulation

Author:

Yu Xin¹,Zhao Jiayi¹^ORCID,Chen Shuo²^ORCID,Huang Diangui¹,Zhang Kaixuan³^ORCID,Cao Damin⁴

Affiliation:

1. School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China

2. School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, People's Republic of China

3. Institute of Biomechanics and Medical Engineering, AML, Department of Engineering Mechanics, Tsinghua University, Beijing, People's Republic of China

4. School of Air Transportation, Shanghai University of Engineering Science, Shanghai, People's Republic of China

Funder

National Natural Science Foundation of China

Shanghai Sailing Program

University of Shanghai for Science and Technology degree program guidance layout and construction cultivation project

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,General Materials Science,General Chemical Engineering,Modeling and Simulation,Information Systems,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/08927022.2022.2055755

Reference34 articles.

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3. Imposition of physical parameters in dissipative particle dynamics

4. Marsh C. Theoretical aspects of dissipative particle dynamics. Reviews in Computational Chemistry; 1998.

5. Energy-conserving dissipative particle dynamics with temperature-dependent properties