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Computer simulators can be computationally intensive to run over a largenumber of input values, as required for optimization and various uncertaintyquantification tasks. The standard paradigm for the design and analysis ofcomputer experiments is to employ Gaussian random fields to model computersimulators. Gaussian process models are trained on input-output data obtainedfrom simulation runs at various input values. Following this approach, wepropose a sequential design algorithm, MICE (Mutual Information for ComputerExperiments), that adaptively selects the input values at which to run thecomputer simulator, in order to maximize the expected information gain (mutualinformation) over the input space. The superior computational efficiency of theMICE algorithm compared to other algorithms is demonstrated by test functions,and a tsunami simulator with overall gains of up to 20% in that case.

Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model