Issue No. 02 - March/April (2007 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MCSE.2007.24
Nicholas Zabaras , Cornell University
Sethuraman Sankaran , Cornell University
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.
maximum entropy, information learning, uncertainty, microstructure models, stochastic models
S. Sankaran and N. Zabaras, "An Information-Theoretic Approach to Stochastic Materials Modeling," in Computing in Science & Engineering, vol. 9, no. , pp. 30-39, 2007.