Title :
An Information-Theoretic Approach to Stochastic Materials Modeling
Author :
Zabaras, Nicholas ; Sankaran, Sethuraman
Author_Institution :
Cornell Univ.
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;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCSE.2007.24