DocumentCode :
1120455
Title :
An Information-Theoretic Approach to Stochastic Materials Modeling
Author :
Zabaras, Nicholas ; Sankaran, Sethuraman
Author_Institution :
Cornell Univ.
Volume :
9
Issue :
2
fYear :
2007
Firstpage :
30
Lastpage :
39
Abstract :
An approach derived from information-theoretic principles can help researchers build stochastic microstructural models. This approach involves extracting topological information from microstructural samples and using this information to build a stochastic model. To generate huge databases of stochastic material models, the authors thus propose using an information-learning algorithm to train a network for statistical outputs
Keywords :
aluminium; crystal microstructure; physics computing; stochastic processes; Al; aluminum polycrystals; information theory; microstructures; stochastic materials handling; stochastic microstructural models; Crystalline materials; Data mining; Entropy; Grain boundaries; Grain size; Inorganic materials; Machine learning; Microstructure; Solid modeling; Stochastic processes; information learning; maximum entropy; microstructure models; stochastic models; uncertainty;
fLanguage :
English
Journal_Title :
Computing in Science & Engineering
Publisher :
ieee
ISSN :
1521-9615
Type :
jour
DOI :
10.1109/MCSE.2007.24
Filename :
4100927
Link To Document :
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