Material parameter identification with parallel processing and geo-applications

Authors:
Radim Blaheta;Rostislav Hrtus;Roman Kohut;Owe Axelsson;Ondřej Jakl
Affiliations:
Institute of Geonics AS CR, Ostrava, Czech Republic;Institute of Geonics AS CR, Ostrava, Czech Republic;Institute of Geonics AS CR, Ostrava, Czech Republic;Institute of Geonics AS CR, Ostrava, Czech Republic;Institute of Geonics AS CR, Ostrava, Czech Republic
Venue:
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Year:
2011

Citing 8
Cited 1

Iterative solution methods

Iterative solution methods
Computational Methods for Inverse Problems

Computational Methods for Inverse Problems
Genetic and Random Search Methods in Optimal Shape Design Problems

Journal of Global Optimization
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
A Parallel Implementation of the Simplex Function Minimization Routine

Computational Economics
Schwarz methods for discrete elliptic and parabolic problems with an application to nuclear waste repository modelling

Mathematics and Computers in Simulation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization

Evolutionary Computation
GEM: a platform for advanced mathematical geosimulations

PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I

Parallel solvers for numerical upscaling

PARA'12 Proceedings of the 11th international conference on Applied Parallel and Scientific Computing

Quantified Score

Hi-index	0.00

Visualization

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.