期刊
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
卷 357, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2019.112594
关键词
Machine learning; Recurrent neural network; Deep learning; Multiscale modeling; Viscoplasticity; Strain softening
资金
- European Research Council under the European Union [617972]
- European Research Council (ERC) [617972] Funding Source: European Research Council (ERC)
FE2 multiscale simulations of history-dependent materials are accelerated by means of a recurrent neural network (RNN) surrogate for the history-dependent micro level response. We propose a simple strategy to efficiently collect stress-strain data from the micro model, and we modify the RNN model such that it resembles a nonlinear finite element analysis procedure during training We then implement the trained RNN model in the FE2 scheme and employ automatic differentiation to compute the consistent tangent. The exceptional performance of the proposed model is demonstrated through a number of academic examples using strain-softening Perzyna viscoplasticity as the nonlinear material model at the micro level. (C) 2019 Elsevier B.V. All rights reserved.
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