  Computer simulations are becoming an essential tool in many scientific fields
from molecular dynamics to aeronautics. In glaciology, future predictions of
sea level change require input from ice sheet models. Due to uncertainties in
the forcings and the parameter choices for such models, many different
realisations of the model are needed in order to produce probabilistic
forecasts of sea level change. For these reasons, producing robust
probabilistic forecasts from an ensemble of model simulations over regions of
interest can be extremely expensive for many ice sheet models. Multi-fidelity
experimental design (MFED) is a strategy that models the high-fidelity output
of the simulator by combining information from various resolutions in an
attempt to minimize the computational costs of the process and maximize the
accuracy of the posterior. In this paper, we present an application of MFED to
an ice-sheet simulatorand demonstrate potential computational savings by
modelling the relationship between spatial resolutions. We also analyze the
behavior of MFED strategies using theoretical results from sub-modular
maximization.
