Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures
Abstract
The work in this paper describes neural network surrogate models for calculating the effective mechanical properties of a periodic composites. The models achieve good accuracy even when only provided with training data sampling a small portion of the design space. As an example, the surrogate models are applied to solving the inverse design problem of finding structures with optimal mechanical properties. The surrogate models are sufficiently accurate to recover optimal solutions in general agreement with established topology optimization methods. However, improvements will be required to develop robust, efficient neural network-based surrogate models and several directions for future research are highlighted here.
- Authors:
-
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Nuclear Energy (NE)
- OSTI Identifier:
- 1632185
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Mechanical Design
- Additional Journal Information:
- Journal Volume: 142; Journal Issue: 2; Journal ID: ISSN 1050-0472
- Publisher:
- ASME
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; design optimization; metamodeling; simulation-based design
Citation Formats
Messner, Mark C. Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures. United States: N. p., 2019.
Web. doi:10.1115/1.4045040.
Messner, Mark C. Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures. United States. https://doi.org/10.1115/1.4045040
Messner, Mark C. Thu .
"Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures". United States. https://doi.org/10.1115/1.4045040. https://www.osti.gov/servlets/purl/1632185.
@article{osti_1632185,
title = {Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures},
author = {Messner, Mark C.},
abstractNote = {The work in this paper describes neural network surrogate models for calculating the effective mechanical properties of a periodic composites. The models achieve good accuracy even when only provided with training data sampling a small portion of the design space. As an example, the surrogate models are applied to solving the inverse design problem of finding structures with optimal mechanical properties. The surrogate models are sufficiently accurate to recover optimal solutions in general agreement with established topology optimization methods. However, improvements will be required to develop robust, efficient neural network-based surrogate models and several directions for future research are highlighted here.},
doi = {10.1115/1.4045040},
journal = {Journal of Mechanical Design},
number = 2,
volume = 142,
place = {United States},
year = {Thu Oct 17 00:00:00 EDT 2019},
month = {Thu Oct 17 00:00:00 EDT 2019}
}
Web of Science
Figures / Tables:
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