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Title: Machine Learning Models of Plastic Flow Based on Representation Theory

Abstract

We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen input-output map. Furthermore, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. With these developments, we enable rapid model building in real-time with experiments, and guide data collection and feature discovery.

Authors:
 [1];  [2];  [2];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States). Mechanics of Materials Dept.
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States). Thermal/Fluid Science and Engineering Dept.
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); SNL Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1502970
Report Number(s):
SAND-2019-2905J
Journal ID: ISSN 1526-1492; 673466
Grant/Contract Number:  
NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Computer Modeling in Engineering & Sciences
Additional Journal Information:
Journal Volume: 117; Journal Issue: 3; Journal ID: ISSN 1526-1492
Publisher:
Tech Science Press
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 42 ENGINEERING; machine learning; neural network; plasticity

Citation Formats

Jones, R. E., Templeton, J. A., Sanders, C. M., and Ostien, J. T. Machine Learning Models of Plastic Flow Based on Representation Theory. United States: N. p., 2018. Web. doi:10.31614/cmes.2018.04285.
Jones, R. E., Templeton, J. A., Sanders, C. M., & Ostien, J. T. Machine Learning Models of Plastic Flow Based on Representation Theory. United States. doi:10.31614/cmes.2018.04285.
Jones, R. E., Templeton, J. A., Sanders, C. M., and Ostien, J. T. Sat . "Machine Learning Models of Plastic Flow Based on Representation Theory". United States. doi:10.31614/cmes.2018.04285.
@article{osti_1502970,
title = {Machine Learning Models of Plastic Flow Based on Representation Theory},
author = {Jones, R. E. and Templeton, J. A. and Sanders, C. M. and Ostien, J. T.},
abstractNote = {We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen input-output map. Furthermore, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. With these developments, we enable rapid model building in real-time with experiments, and guide data collection and feature discovery.},
doi = {10.31614/cmes.2018.04285},
journal = {Computer Modeling in Engineering & Sciences},
number = 3,
volume = 117,
place = {United States},
year = {2018},
month = {12}
}

Journal Article:
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This content will become publicly available on December 29, 2019
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