Improved surrogate-based optimization of a marine ecosystem model using response correction

SIMULTECH 2011 - Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Volumn , Issue , 2011, Pages 449-457

  Priess M ^a   Koziel, S ^b   Slawig, T ^a  

^a UNIVERSITY OF KIEL   (Germany)

^b REYKJAVIK UNIVERSITY   (Iceland)

Author keywords

Climate models; Marine ecosystem models; Parameter optimization; Response correction; Surrogate based optimization

Indexed keywords

COMPUTATIONAL COSTS; CORRECTION SCHEMES; CORRECTION TECHNIQUES; ECOSYSTEM MODEL; HIGH FIDELITY; HIGH FIDELITY MODELS; LOW-FIDELITY MODELS; MARINE ECOSYSTEM; MODEL PARAMETERS; OPTIMIZATION PROCESS; PARAMETER OPTIMIZATION; PHYSICS-BASED; RESPONSE CORRECTION; SIMPLE MODIFICATIONS; SURROGATE MODEL; SURROGATE-BASED OPTIMIZATION; TEST CASE;

CLIMATE MODELS; COMPUTER SIMULATION; MARINE APPLICATIONS; OPTIMIZATION;

ECOSYSTEMS;

EID: 80052563137     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Bandler, J. W., Cheng, Q. S., Dakroury, S. A., Mohamed, A. S., Bakr, M. H., Madsen, K., and Søndergaard, J. (2004). Space mapping: The state of the art. IEEE T. Microw. Theory., 52(1).
2. Banks, H. T. and Kunisch, K. (1989). Estimation Techniques for Distributed Parameter Systems. Birkhäuser.
3. Conn, A. R., Gould, N. I. M., and Toint, P. L. (2000). Trustregion methods. Society for Industrial and Applied Mathematics, Philadelphia, PA.
4. Fennel, W. and Neumann, T. (2004). Introduction to the Modelling of Marine Ecosystems. Elsevier.
5. Forrester, A. I. and Keane, A. J. (2009). Recent advances in surrogate-based optimization. Prog. Aerosp. Sci., 45(1-3):50-79.
6. Gill, A. E. (1982). Atmosphere - Ocean Dynamics, volume 30 of International Geophysics Series. Academic Press.
7. Koziel, S., Bandler, J. W., and Cheng, Q. S. (2010). Robust trust-region space-mapping algorithms for microwave design optimization. IEEE T. Microw. Theory., 58(8):2166-2174.
8. Leifsson, L. and Koziel, S. (2010). Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction. Journal of Computational Science, 1(2):98-106.
9. Majda, A. (2003). Introduction to PDE's and Waves for the Atmosphere and Ocean. AMS.
10. Marchuk, G. I. (1982). Methods of Numerical Mathematics. Springer, 2nd edition.
11. McGuffie, K. and Henderson-Sellers, A. (2005). A Climate Modelling Primer. Wiley, 3nd edition.
12. Oschlies, A. and Garcon, V. (1999). An eddy-permitting coupled physical-biological model of the north atlantic. 1. sensitivity to advection numerics and mixed layer physics. Global Biogeochem. Cy., 13:135-160. (Pubitemid 29365970)
13. Prieß, M., Koziel, S., and Slawig, T. (2011). Surrogate-based optimization of climate model parameters using response correction. Preprint 1104, Christian-Albrechts-Universität zu Kiel, Institute for Computer Science.
14. Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., and Tucker, P. K. (2005). Surrogate-based analysis and optimization. Prog. Aerosp. Sci., 41(1):1-28. (Pubitemid 40537187)
15. Rückelt, J., Sauerland, V., Slawig, T., Srivastav, A., Ward, B., and Patvardhan, C. (2010). Parameter optimization and uncertainty analysis in a model of oceanic co2-uptake using a hybrid algorithm and algorithmic differentiation. Nonlinear Analysis: Real World Applications, online.
16. Sarmiento, J. and Gruber, N. (2006). Ocean Biogeochemi-cal Dynamics. Princeton University Press.
17. Simpson, T., Poplinski, J., Koch, P. N., and Allen, J. (2001). Metamodels for computer-based engineering design: Survey and recommendations. Eng. Comput., 17:129-150. 10.1007/PL00007198. (Pubitemid 32659225)
18. Smola, A. J. and Schölkopf, B. (2004). A tutorial on support vector regression. Stat. Comput., 14:199-222. 10.1023/B:STCO.0000035301.49549.88. (Pubitemid 39063488)
19. Søndergaard, J. (2003). Optimization using surrogate models - by the space mapping technique. PhD thesis, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby. Supervisor: Kaj Madsen.
20. Tarantola, A. (2005). Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM.