R. E. Jones1,*, J. A. Templeton1, C. M. Sanders1, J. T. Ostien1
CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 309-342, 2018, DOI:10.31614/cmes.2018.04285
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 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… More >
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