  Direct numerical simulation of hierarchical materials via
homogenization-based concurrent multiscale models poses critical challenges for
3D large scale engineering applications, as the computation of highly nonlinear
and path-dependent material constitutive responses at the lower scale causes
prohibitively high computational costs. In this work, we propose a
physics-informed data-driven deep learning model as an efficient surrogate to
emulate the effective responses of heterogeneous microstructures under
irreversible elasto-plastic hardening and softening deformation. Our
contribution contains several major innovations. First, we propose a novel
training scheme to generate arbitrary loading sequences in the sampling space
confined by deformation constraints where the simulation cost of homogenizing
microstructural responses per sequence is dramatically reduced via mechanistic
reduced-order models. Second, we develop a new sequential learner that
incorporates thermodynamics consistent physics constraints by customizing
training loss function and data flow architecture. We additionally demonstrate
the integration of trained surrogate within the framework of classic multiscale
finite element solver. Our numerical experiments indicate that our model shows
a significant accuracy improvement over pure data-driven emulator and a
dramatic efficiency boost than reduced models. We believe our data-driven model
provides a computationally efficient and mechanics consistent alternative for
classic constitutive laws beneficial for potential high-throughput simulations
that needs material homogenization of irreversible behaviors.
