Beck, J; Guillas, S; (2016) Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model. SIAM/ASA Journal on Uncertainty Quantification , 4 (1) pp. 739-766. 10.1137/140989613.

Preview

Text 140989613.pdf - Published Version Download (507kB) | Preview

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 by a tsunami simulator with overall gains of up to 20% in that case.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item