Publications

@article{Ghavamian2019,

title = {Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network},

author = {F. Ghavamian and A. Simone},

doi = {10.1016/j.cma.2019.112594},

year  = {2019},

date = {2019-01-01},

journal = {COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING},

volume = {357},

publisher = {Elsevier B.V.},

abstract = {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 FE 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.},

keywords = {Deep learning, Machine learning, Multiscale modeling, Recurrent neural network, Strain softening, Viscoplasticity},

pubstate = {published},

tppubtype = {article}

}