  The construction of computer models (mathematical models implemented in
computer codes), with respect to observed phenomena, is usually undertaken by
building different variants depending on modeller sensibility, and choosing the
one yielding the best fit of the field data, according to Root Mean Squared
Error (RMSE) based measures. Usually a particular model is chosen because of
its marginally lower RMSE, and not because of its actual higher adequacy,
risking that its capability of extrapolating predictions is poor. This work
aims at improving the current practice in the creation of computer models by
proposing an approach similar to those employed in statistical modelling,
wherein starting from the simplest hypothesis, effective model upgrades are
identified by analysing discrepancies between observations and predictions, and
different model variants are compared according to robust likelihood based
criteria. The method, focused on models with high dimensional outputs and
boundary conditions and centred on Bayesian calibration, is demonstrated on
numerical experiments considering a series of building energy models. The
object of the modelling is a test facility used for round robin tests in the
context of the International Energy Agency (IEA), Energy Building and
Communities (EBC), Annex 58.
