An Information-Theoretic Approach for Computational Material Modeling
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.
Wei Yang, Mamtimin Geni, Tiejun Wang and Zhuo Zhuang
T. Furukawa and J. G. Michopoulos, "An Information-Theoretic Approach for Computational Material Modeling", Advanced Materials Research, Vols. 33-37, pp. 857-862, 2008