  Within the framework of computational plasticity, recent advances show that
the quasi-static response of an elasto-plastic structure under cyclic loadings
may exhibit a time multiscale behaviour. In particular, the system response can
be computed in terms of time microscale and macroscale modes using a weakly
intrusive multi-time Proper Generalized Decomposition (MT-PGD). In this work,
such micro-macro characterization of the time response is exploited to build a
data-driven model of the elasto-plastic constitutive relation. This can be
viewed as a predictor-corrector scheme where the prediction is driven by the
macrotime evolution and the correction is performed via a sparse sampling in
space. Once the nonlinear term is forecasted, the multi-time PGD algorithm
allows the fast computation of the total strain. The algorithm shows
considerable gains in terms of computational time, opening new perspectives in
the numerical simulation of history-dependent problems defined in very large
time intervals.
