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.
|Julkaisu||INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 2016|
|OKM-julkaisutyyppi||A1 Julkaistu artikkeli, soviteltu|