Machine Learning Models of Plastic Flow Based on Representation Theory

Jones, R. E.; Templeton, J. A.; Sanders, C. M.; Ostien, J. T.

doi:10.31614/cmes.2018.04285

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:

Jones, R. E. ^[1]; Templeton, J. A. ^[2]; Sanders, C. M. ^[2]; Ostien, J. T. ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States). Mechanics of Materials Dept.
Sandia National Lab. (SNL-CA), Livermore, CA (United States). Thermal/Fluid Science and Engineering Dept.

Publication Date:: Sat Dec 29 00:00:00 EST 2018

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.

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                    Jones, R. E., Templeton, J. A., Sanders, C. M., & Ostien, J. T. Machine Learning Models of Plastic Flow Based on Representation Theory.  United States.  https://doi.org/10.31614/cmes.2018.04285

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                    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.  https://doi.org/10.31614/cmes.2018.04285.  https://www.osti.gov/servlets/purl/1502970.

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@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         = {Sat Dec 29 00:00:00 EST 2018},

  month        = {Sat Dec 29 00:00:00 EST 2018}

}

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Works referenced in this record:

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Furukawa, Tomonari; Yagawa, Genki
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Theocaris, P. S.; Panagiotopoulos, P. D.
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Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304
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Desu, Raghuram Karthik; Guntuku, Sharath Chandra; B., Aditya
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Title: Machine Learning Models of Plastic Flow Based on Representation Theory

Abstract

Citation Formats

Implicit constitutive modelling for viscoplasticity using neural networks journal, September 1998

Gaussian approximation potentials: A brief tutorial introduction journal, April 2015

Stress-deformation relations for anisotropic solids journal, January 1957

On the application of dual variables in continuum mechanics journal, January 1989

Neural networks for computing in fracture mechanics. Methods and prospects of applications journal, July 1993

Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304 journal, January 2014

Extremal Systems of Points and Numerical Integration on the Sphere journal, July 2004

The anisotropic tensors journal, January 1957

Further Remarks on the Stress-Deformation Relations for Isotropic Materials journal, January 1955

Neurobiological Computational Models in Structural Analysis and Design conference, April 1990

Implicit constitutive modelling for viscoplasticity using neural networks
journal, September 1998

Gaussian approximation potentials: A brief tutorial introduction
journal, April 2015

Stress-deformation relations for anisotropic solids
journal, January 1957

On the application of dual variables in continuum mechanics
journal, January 1989

Neural networks for computing in fracture mechanics. Methods and prospects of applications
journal, July 1993

Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304
journal, January 2014

Extremal Systems of Points and Numerical Integration on the Sphere
journal, July 2004

The anisotropic tensors
journal, January 1957

Further Remarks on the Stress-Deformation Relations for Isotropic Materials
journal, January 1955

Neurobiological Computational Models in Structural Analysis and Design
conference, April 1990