  History Matching is a technique used to calibrate complex computer models,
that is, finding the input settings which lead to the simulated output matching
up with real world observations. Key to this technique is the construction of
emulators, which provide fast probabilistic predictions of future simulations.
In this work, we adapt the History Matching framework to tackle the problem of
level set estimation, that is, finding input settings where the output is below
(or above) some threshold. The developed methodology is heavily motivated by a
specific case study: how can one design a building that will be sufficiently
protected against overheating and sufficiently energy efficient, whilst
considering the expected increases in temperature due to climate change? We
successfully manage to address this - greatly reducing a large initial set of
candidate building designs down to a small set of acceptable potential
buildings.
