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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
04 Aug 2014
Review status
This discussion paper is a preprint. It has been under review for the journal Nonlinear Processes in Geophysics (NPG). The revised manuscript was not accepted.
Bayesian optimization for tuning chaotic systems
M. Abbas1, A. Ilin1, A. Solonen2, J. Hakkarainen3, E. Oja1, and H. Järvinen4 1Aalto University, School of Science, Espoo, Finland
2Lappeenranta University of Technology, Lappeenranta, Finland
3Finnish Meterological Institute, Helsinki, Finland
4University of Helsinki, Helsinki, Finland
Abstract. In this work, we consider the Bayesian optimization (BO) approach for tuning parameters 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 and without the need of any gradient information.

Citation: Abbas, M., Ilin, A., Solonen, A., Hakkarainen, J., Oja, E., and Järvinen, H.: Bayesian optimization for tuning chaotic systems, Nonlin. Processes Geophys. Discuss., 1, 1283-1312,, 2014.
M. Abbas et al.


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