Abstract
In the Community of Computational Materials Science, One of the Challenges in Hierarchical Multiscale Modeling is Information-Passing from One Scale to Another, especially from the Molecular Model to the Continuum Model. a Machine-Learning-Enhanced Approach, Proposed in This Paper, Provides an Alternative Solution. in the Developed Hierarchical Multiscale Method, Molecular Dynamics Simulations in the Molecular Model Are Conducted First to Generate Datasets, Which Represents Physical Phenomena at the Nanoscale. the Datasets Are Then Used to Train Neural Networks for Failure Classification and Stress Regressions. Finally, the Well-Trained Learning Machines Are Implemented in the Continuum Model to Study the Mechanical Behaviors of Materials at the Macroscale. Randomized Neural Networks Are Employed Due to their Computational Efficiency.
Recommended Citation
S. Xiao et al., "Data-Enabled Computational Multiscale Method in Materials Science and Engineering," Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, pp. 1123 - 1128, article no. 8947829, Institute of Electrical and Electronics Engineers, Dec 2018.
The definitive version is available at https://doi.org/10.1109/CSCI46756.2018.00217
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Materials science; Molecular dynamics; Multiscale; Randomized neural networks
International Standard Book Number (ISBN)
978-172811360-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2018
Comments
National Science Foundation, Grant CMMI-1537512