Abstract
We have already shown in a previous methodological work that the surrogate-based optimization (SBO) approach can be successful and computationally very efficient when reconstructing parameters in a typical nonlinear, time-dependent marine ecosystem model, where a one-dimensional application has been considered to test the method's functionality in a first step. The application on real (measurement) data is covered in this paper. Essential here are a special model data treatment and further methodological enhancements which allow us to improve the robustness of the algorithm and the accuracy of the solution. By numerical experiments, we demonstrate that SBO is able to yield a solution close to the original model's optimum while time savings are again up to 85% when compared to a conventional direct optimization of the original model.
| Original language | English |
|---|---|
| Pages (from-to) | 423-437 |
| Number of pages | 15 |
| Journal | Journal of Computational Science |
| Volume | 4 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2013 |
Bibliographical note
Funding Information: The authors would like to thank Prof. Andreas Oschlies from GEOMAR in Kiel for his support to this research as well as the anonymous reviewers for their constructive and useful comments which helped to improve the manuscript. The authors further acknowledge funding to this research by the DFG Cluster of Excellence “The Future Ocean”.Other keywords
- Low-fidelity model
- Marine ecosystem model
- Model calibration
- Parameter identification
- Response correction
- Surrogate-based optimization