Conference proceeding
Data-Enabled Computational Multiscale Method in Materials Science and Engineering
2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp.1123-1128
12/2018
DOI: 10.1109/CSCI46756.2018.00217
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.
Details
- Title: Subtitle
- Data-Enabled Computational Multiscale Method in Materials Science and Engineering
- Creators
- Shaoping Xiao - University of IowaAmaury Lendasse - University of HoustonRenjie Hu - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp.1123-1128
- DOI
- 10.1109/CSCI46756.2018.00217
- Publisher
- IEEE
- Language
- English
- Date published
- 12/2018
- Academic Unit
- Iowa Technology Institute; Mechanical Engineering
- Record Identifier
- 9984196654102771
Metrics
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