Empirical evaluation of bayesian optimization in parametric tuning of chaotic systems

Mudassar Abbas, Alexander Ilin, Antti Solonen, Janne Hakkarainen, Erkki Oja, Heikki Järvinen

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)467-485
Issue number6
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed


  • Bayesian optimization
  • chaotic systems
  • data assimilation
  • Ensemble Kalman filter


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