  In this paper, a methodology for fine scale modeling of large scale
structures is proposed, which combines the variational multiscale method,
domain decomposition and model order reduction. The influence of the fine scale
on the coarse scale is modelled by the use of an additive split of the
displacement field, addressing applications without a clear scale separation.
Local reduced spaces are constructed by solving an oversampling problem with
random boundary conditions. Herein, we inform the boundary conditions by a
global reduced problem and compare our approach using physically meaningful
correlated samples with existing approaches using uncorrelated samples. The
local spaces are designed such that the local contribution of each subdomain
can be coupled in a conforming way, which also preserves the sparsity pattern
of standard finite element assembly procedures. Several numerical experiments
show the accuracy and efficiency of the method, as well as its potential to
reduce the size of the local spaces and the number of training samples compared
to the uncorrelated sampling.
