RT Journal Article ID 27b322655584ada2 A1 Abbas, Mudassar A1 Ilin, Alexander A1 Solonen, Antti A1 Hakkarainen, Janne A1 Oja, Erkki A1 Jarvinen, Heikki T1 EMPIRICAL EVALUATION OF BAYESIAN OPTIMIZATION IN PARAMETRIC TUNING OF CHAOTIC SYSTEMS JF International Journal for Uncertainty Quantification JO IJUQ YR 2016 FD 2017-01-06 VO 6 IS 6 SP 467 OP 485 K1 Bayesian optimization K1 chaotic systems K1 data assimilation K1 ensemble Kalman filter AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,3bc93f646a4f7eac,27b322655584ada2.html