Выходит 6 номеров в год
ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099
Indexed in
EMPIRICAL EVALUATION OF BAYESIAN OPTIMIZATION IN PARAMETRIC TUNING OF CHAOTIC SYSTEMS
Краткое описание
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.
-
Rosalie Martin, Kieffer Emmanuel, Brust Matthias R., Danoy Grégoire, Bouvry Pascal, Bayesian optimisation to select Rössler system parameters used in Chaotic Ant Colony Optimisation for Coverage, Journal of Computational Science, 41, 2020. Crossref
-
Lunderman Spencer, Morzfeld Matthias, Posselt Derek J., Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above, Tellus A: Dynamic Meteorology and Oceanography, 73, 1, 2021. Crossref
-
Bradley William, Kim Jinhyeun, Kilwein Zachary, Blakely Logan, Eydenberg Michael, Jalvin Jordan, Laird Carl, Boukouvala Fani, Perspectives on the integration between first-principles and data-driven modeling, Computers & Chemical Engineering, 166, 2022. Crossref