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 STYLE
Abbas, M., Ilin, A., Solonen, A., Hakkarainen, J., Oja, E., & Järvinen, H. (2014). Bayesian optimization for tuning chaotic systems. Nonlinear Processes in Geophysics Discussions, 1(2), 1283–1312.
Mendeley helps you to discover research relevant for your work.