  Fast machine learning-based surrogate models are trained to emulate slow,
high-fidelity engineering simulation models to accelerate engineering design
tasks. This introduces uncertainty as the surrogate is only an approximation of
the original model.
  Bayesian methods can quantify that uncertainty, and deep learning models
exist that follow the Bayesian paradigm. These models, namely Bayesian neural
networks and Gaussian process models, enable us to give predictions together
with an estimate of the model's uncertainty. As a result we can derive
uncertainty-aware surrogate models that can automatically suspect unseen design
samples that cause large emulation errors. For these samples, the high-fidelity
model can be queried instead. This outlines how the Bayesian paradigm allows us
to hybridize fast, but approximate, and slow, but accurate models.
  In this paper, we train two types of Bayesian models, dropout neural networks
and stochastic variational Gaussian Process models, to emulate a complex high
dimensional building energy performance simulation problem. The surrogate model
processes 35 building design parameters (inputs) to estimate 12 different
performance metrics (outputs). We benchmark both approaches, prove their
accuracy to be competitive, and show that errors can be reduced by up to 30%
when the 10% of samples with the highest uncertainty are transferred to the
high-fidelity model.
