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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/npgd-1-1283-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npgd-1-1283-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  04 Aug 2014

04 Aug 2014

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This preprint was under review for the journal NPG but the revision was not accepted.

Bayesian optimization for tuning chaotic systems

M. Abbas1, A. Ilin1, A. Solonen2, J. Hakkarainen3, E. Oja1, and H. Järvinen4 M. Abbas et al.
  • 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.

M. Abbas et al.

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