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A thresholded Gaussian random field model is developed for the microstructureof porous materials. Defining the random field as a solution to stochasticpartial differential equation allows for flexible modelling ofnon-stationarities in the material and facilitates computationally efficientmethods for simulation and model fitting. A Markov Chain Monte Carlo algorithmis developed and used to fit the model to three-dimensional confocal laserscanning microscopy images. The methods are applied to study a porousethylcellulose/hydroxypropylcellulose polymer blend that is used as a coatingto control drug release from pharmaceutical tablets. The aim is to investigatehow mass transport through the material depends on the microstructure. Wederive a number of goodness-of-fit measures based on numerically calculateddiffusion through the material. These are used in combination with measuresthat characterize the geometry of the pore structure to assess model fit. Themodel is found to fit stationary parts of the material well.

A three-dimensional statistical model for imaged microstructures of porous polymer films