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| Titel |
Bayesian optimization for tuning chaotic systems |
| VerfasserIn |
M. Abbas, A. Ilin, A. Solonen, J. Hakkarainen, E. Oja, H. Järvinen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 2 ; Nr. 1, no. 2 (2014-08-04), S.1283-1312 |
| Datensatznummer |
250115118
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| Publikation (Nr.) |
 copernicus.org/npgd-1-1283-2014.pdf |
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| Zusammenfassung |
| 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. |
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