# Parameters identification of material models based on the inverse analysis

Danuta Szeliga; Jerzy Gawąd; Maciej Pietrzyk

International Journal of Applied Mathematics and Computer Science (2004)

- Volume: 14, Issue: 4, page 549-556
- ISSN: 1641-876X

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topSzeliga, Danuta, Gawąd, Jerzy, and Pietrzyk, Maciej. "Parameters identification of material models based on the inverse analysis." International Journal of Applied Mathematics and Computer Science 14.4 (2004): 549-556. <http://eudml.org/doc/207719>.

@article{Szeliga2004,

abstract = {The paper presents an application of the inverse analysis to the identification of two models: a phase transformation model and a rheological model. The optimization algorithm for the inverse analysis was tested for various techniques of searching for the minimum: derivative-free and gradient methods, as well as genetic algorithms. Simulation results were validated for microalloyed niobium steel. An optimization strategy, which is adequate for the inverse analysis, is suggested.},

author = {Szeliga, Danuta, Gawąd, Jerzy, Pietrzyk, Maciej},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {inverse analysis; phase transformation; internal variable model; optimization; gradient methods; genetic algorithms},

language = {eng},

number = {4},

pages = {549-556},

title = {Parameters identification of material models based on the inverse analysis},

url = {http://eudml.org/doc/207719},

volume = {14},

year = {2004},

}

TY - JOUR

AU - Szeliga, Danuta

AU - Gawąd, Jerzy

AU - Pietrzyk, Maciej

TI - Parameters identification of material models based on the inverse analysis

JO - International Journal of Applied Mathematics and Computer Science

PY - 2004

VL - 14

IS - 4

SP - 549

EP - 556

AB - The paper presents an application of the inverse analysis to the identification of two models: a phase transformation model and a rheological model. The optimization algorithm for the inverse analysis was tested for various techniques of searching for the minimum: derivative-free and gradient methods, as well as genetic algorithms. Simulation results were validated for microalloyed niobium steel. An optimization strategy, which is adequate for the inverse analysis, is suggested.

LA - eng

KW - inverse analysis; phase transformation; internal variable model; optimization; gradient methods; genetic algorithms

UR - http://eudml.org/doc/207719

ER -

## References

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