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International Journal for Uncertainty Quantification
Factor de Impacto: 0.967 Factor de Impacto de 5 años: 1.301 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Imprimir: 2152-5080
ISSN En Línea: 2152-5099

Acceso abierto

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2016016645
pages 467-485

EMPIRICAL EVALUATION OF BAYESIAN OPTIMIZATION IN PARAMETRIC TUNING OF CHAOTIC SYSTEMS

Mudassar Abbas
Department of Computer Science, School of Science, Aalto University, Espoo, Finland
Alexander Ilin
Department of Computer Science, School of Science, Aalto University, Espoo, Finland
Antti Solonen
Lappeenranta University of Technology, Laboratory of Applied Mathematics
Janne Hakkarainen
Finnish Meterological Institute, Helsinki, Finland
Erkki Oja
Department of Computer Science, School of Science, Aalto University, Espoo, Finland
Heikki Jarvinen
University of Helsinki, Helsinki, Finland

SINOPSIS

In this work, we consider the Bayesian optimization (BO) approach for parametric tuning of complex chaotic systems. Such problems arise, for instance, in tuning the sub-grid-scale parameterizations in weather and climate models. For such problems, the tuning procedure is generally based on a performance metric which measures how well the tuned model fits the data. This tuning is often a computationally expensive task. We show that BO, as a tool for finding the extrema of computationally expensive objective functions, is suitable for such tuning tasks. In the experiments, we consider tuning parameters of two systems: a simplified atmospheric model and a low-dimensional chaotic system. We show that BO is able to tune parameters of both the systems with a low number of objective function evaluations.


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