Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Learning from Examples with Information Theoretic Criteria
Journal of VLSI Signal Processing Systems
A non-linear dimension reduction methodology for generating data-driven stochastic input models
Journal of Computational Physics
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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.