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Marine Ecosystem Model Calibration through Enhanced Surrogate-Based Optimization

  • Malte PrießEmail author
  • Slawomir Koziel
  • Thomas Slawig
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 197)

Abstract

Mathematical optimization of models based on simulations usually requires a substantial number of computationally expensive model evaluations and it is therefore often impractical. An improved surrogate-based optimization methodology, which addresses these issues, is developed for the optimization of a representative of the class of one-dimensional marine ecosystem models. Our technique is based upon a multiplicative response correction technique to create a computationally cheap but yet reasonably accurate surrogate from a temporarily coarser discretized physics-based coarse model. The original version of this methodology was capable of yielding about 84% computational cost savings when compared to the fine ecosystem model optimization. Here, we demonstrate that by employing relatively simple modifications, the surrogate model accuracy and the efficiency of the optimization process can be further improved. More specifically, for the considered test case, the optimization cost is reduced three times, i.e., from about 15% to only 5% of the cost of the direct fine model optimization.

Keywords

Marine Ecosystem Models Surrogate-based Optimization Parameter Optimization Response Correction Data Assimilation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute for Computer ScienceCluster The Future Ocean, Christian-Albrechts Universität zu KielKielGermany
  2. 2.Engineering Optimization & Modeling Center, School of Science and EngineeringReykjavik UniversityReykjavikIceland

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