A deep material network approach for predicting the thermomechanical response of composites
Dongil Shin, Ryan Alberdi, Ricardo A. Lebensohn, Rémi Dingreville
Abstract
Recent progress combining micromechanics and machine learning holds promise for accurately and rapidly predicting the mechanical behavior of complex composite materials. This study extends the Deep Material Network (DMN), a binary-tree network trained on linear direct-numerical-simulation data, to predict the thermo-elasto-viscoplastic behavior of composites. This extension incorporates the thermal expansion properties homogenization into the network’s building blocks and expands the online formulation to consider thermal boundary conditions. By comparing various implementations of these building blocks, we demonstrate the network’s extrapolation to thermomechanical problems. We then show how this extended DMN can be applied to uncertainty quantification and inverse design problems.