  Multiscale computational modelling is challenging due to the high
computational cost of direct numerical simulation by finite elements. To
address this issue, concurrent multiscale methods use the solution of cheaper
macroscale surrogates as boundary conditions to microscale sliding windows. The
microscale problems remain a numerically challenging operation both in terms of
implementation and cost. In this work we propose to replace the local
microscale solution by an Encoder-Decoder Convolutional Neural Network that
will generate fine-scale stress corrections to coarse predictions around
unresolved microscale features, without prior parametrisation of local
microscale problems. We deploy a Bayesian approach providing credible intervals
to evaluate the uncertainty of the predictions, which is then used to
investigate the merits of a selective learning framework. We will demonstrate
the capability of the approach to predict equivalent stress fields in porous
structures using linearised and finite strain elasticity theories.
