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Machine Learning Models of Plastic Flow Based on Representation Theory

R. E. Jones1,*, J. A. Templeton1, C. M. Sanders1, J. T. Ostien1

Sandia National Laboratories, P.O. Box 969, Livermore, CA 94551, USA.

*Corresponding Author: R. E. Jones. Email: .

(This article belongs to this Special Issue: Data-driven Computational Modeling and Simulations)

Computer Modeling in Engineering & Sciences 2018, 117(3), 309-342.


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 inputoutput map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. In the context of the results of numerous simulations, we discuss the design, stability and accuracy of constitutive NNs trained on typical experimental data. With these developments, we enable rapid model building in real-time with experiments, and guide data collection and feature discovery.


Cite This Article

Jones, R. E., Templeton, J. A., Sanders, C. M., Ostien, J. T. (2018). Machine Learning Models of Plastic Flow Based on Representation Theory. CMES-Computer Modeling in Engineering & Sciences, 117(3), 309–342.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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