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An Information-Theoretic Approach to Stochastic Materials Modeling
March/April 2007 (vol. 9 no. 2)
pp. 30-39
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.
Index Terms:
maximum entropy, information learning, uncertainty, microstructure models, stochastic models
Citation:
Nicholas Zabaras, Sethuraman Sankaran, "An Information-Theoretic Approach to Stochastic Materials Modeling," Computing in Science and Engineering, vol. 9, no. 2, pp. 30-39, March-April 2007, doi:10.1109/MCSE.2007.24
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