An Information-Theoretic Approach for Computational Material Modeling

Abstract:

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This paper presents an information-theoretic approach for computational material modeling, which characterizes materials by effectively utilizing all the known information including prior and empirical information. The approach is built within the framework of recursive Bayesian estimation where various inverse analysis techniques, such as Singular Value Decomposition (SVD) and Kalman Filter (KF) can be implemented. Numerical examples first investigate the validity of the proposed approach via parametric studies. The proposed approach has been then successfully applied to the identification of a composite specimen using a triaxial testing machine.

Info:

Periodical:

Advanced Materials Research (Volumes 33-37)

Edited by:

Wei Yang, Mamtimin Geni, Tiejun Wang and Zhuo Zhuang

Pages:

857-862

DOI:

10.4028/www.scientific.net/AMR.33-37.857

Citation:

T. Furukawa and J. G. Michopoulos, "An Information-Theoretic Approach for Computational Material Modeling", Advanced Materials Research, Vols. 33-37, pp. 857-862, 2008

Online since:

March 2008

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Price:

$35.00

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