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

TL;DR

The paper introduces MICE, a sequential design algorithm that efficiently selects input points for computer experiments by maximizing mutual information, demonstrated on test functions and a tsunami simulator with significant gains.

Contribution

It proposes a novel adaptive sequential design method, MICE, that improves efficiency in computer experiment input selection by maximizing mutual information.

Findings

MICE outperforms existing algorithms in computational efficiency.

MICE achieves up to 20% improvement on a tsunami simulator.

Test functions confirm the effectiveness of the MICE algorithm.

Abstract

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