%0 Journal Article %A Abbas, Mudassar %A Ilin, Alexander %A Solonen, Antti %A Hakkarainen, Janne %A Oja, Erkki %A Jarvinen, Heikki %D 2016 %I Begell House %K Bayesian optimization, chaotic systems, data assimilation, ensemble Kalman filter %N 6 %P 467-485 %R 10.1615/Int.J.UncertaintyQuantification.2016016645 %T EMPIRICAL EVALUATION OF BAYESIAN OPTIMIZATION IN PARAMETRIC TUNING OF CHAOTIC SYSTEMS %U https://www.dl.begellhouse.com/journals/52034eb04b657aea,3bc93f646a4f7eac,27b322655584ada2.html %V 6 %X 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. %8 2017-01-06