Data-driven selection of tessellation models describing polycrystalline microstructures
نویسندگان
چکیده
Grain microstructures in polycrystalline materials can be approximated by tessellation models known from stochastic geometry. However, for a given set of microstructural data, the choice of a particular model is traditionally based on researcher intuition. The model should provide a sufficiently good approximation to the data, while keeping the number of parameters small. In this paper, we discuss general aspects of the process of model selection and suggest several criteria for selecting an appropriate candidate from a certain set of tessellation models. The choice of candidate represents a trade-off between accuracy and complexity of the model. Model fitting is carried out by simulated annealing, applied in a consistent manner to twelve different tessellation models.
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