PPAM 2011: Parallel Processing and Applied Mathematics pp 366-375 | Cite as
Material Parameter Identification with Parallel Processing and Geo-applications
Conference paper
Abstract
The paper describes numerical solution of material parameter identification problems, which arise in geo-applications and many other fields. We describe approach based on nonlinear least squares minimization using different optimization techniques (Nelder-Mead, gradient methods, genetic algorithms) as well as experience with OpenMP+MPI parallelization of the solution methods.
Keywords
Parameter identification Nelder-Mead gradient methods genetic algorithms parallelizationPreview
Unable to display preview. Download preview PDF.
References
- 1.Andersson, J.C., Fälth, B., Kristensson, O.: Äspö pillar stability experiment TM back calculation. In: Advances on Coupled Thermo-Hydro-Mechanical-Chemical Processes in Geosystems and Engineering, pp. 675–680. HoHai University, Nanjing, China (2006)Google Scholar
- 2.Axelsson, O.: Iterative Solution Methods. Cambridge University Press, Cambridge (1994)MATHGoogle Scholar
- 3.Blaheta, R., Kohut, R., Neytcheva, M., Starý, J.: Schwarz methods for discrete elliptic and parabolic problems with an application to nuclear waste repository modelling. Mathematics and Computers in Simulation 76, 18–27 (2007)MathSciNetMATHCrossRefGoogle Scholar
- 4.Blaheta, R., Jakl, O., Kohut, R., Starý, J.: GEM – A Platform for Advanced Mathematical Geosimulations. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2009. LNCS, vol. 6067, pp. 266–275. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 5.Cantu-Paz, E.: A Survey of Parallel Genetic Algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10(2), 141–171 (1998)Google Scholar
- 6.Dennis, J.E., Schnabel, R.B.: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. SIAM, Philadelphia (1996)MATHCrossRefGoogle Scholar
- 7.Dennis, J.E., Torczon, V.: Direct search methods on parallel machines. SIAM J. Optimization 1, 448–474 (1991)MathSciNetMATHCrossRefGoogle Scholar
- 8.Haslinger, J., Jedelsky, D., Kozubek, T., Tvrdik, J.: Genetic and Random Search Methods in Optimal Shape Design Problems. Journal of Global Optimization 16, 109–131 (2000)MathSciNetMATHCrossRefGoogle Scholar
- 9.Kelley, C.T.: Iterative Methods for Optimization. SIAM, Philadelphia (1999)MATHCrossRefGoogle Scholar
- 10.Kolda, T.G., Lewis, R.M., Torczon, V.: Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods. SIAM Review 45, 385–482 (2003)MathSciNetMATHCrossRefGoogle Scholar
- 11.Lee, D., Wiswall, M.: A Parallel Implementation of the Simplex Function Minimization Routine. Comput. Econ. 30, 171–187 (2007)MATHCrossRefGoogle Scholar
- 12.Mhlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm, I. Continuous parameter optimization. Evolutionary Computation 1(1), 25–49 (1993)CrossRefGoogle Scholar
- 13.Nocedal, J., Wright, S.J.: Numerical Optimization. Springer (2006)Google Scholar
- 14.Toselli, A., Widlund, O.: Domain Decomposition Methods - Algorithms and Theory. Springer, Berlin (2005)MATHGoogle Scholar
- 15.Vogel, C.R.: Computational Methods for Inverse Problems. Frontiers in Applied Mathematics, vol. (23). SIAM, Philadelphia (2002)MATHCrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2012