2017/05/10 by Sandra Barman, Barman, Sandra, David Bolin +1
Computer Science · Engineering · Environmental Science · Mathematics · #Advanced Numerical Analysis Techniques #Applications (stat.AP) #Computer Graphics and Visualization Techniques #FOS: Computer and information sciences #Groundwater flow and contamination studies #Soil Geostatistics and Mapping #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1705.03938
openalex publication_date 2017/05/10 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
A thresholded Gaussian random field model is developed for the microstructure\nof porous materials. Defining the random field as a solution to stochastic\npartial differential equation allows for flexible modelling of\nnon-stationarities in the material and facilitates computationally efficient\nmethods for simulation and model fitting. A Markov Chain Monte Carlo algorithm\nis developed and used to fit the model to three-dimensional confocal laser\nscanning microscopy images. The methods are applied to study a porous\nethylcellulose/hydroxypropylcellulose polymer blend that is used as a coating\nto control drug release from pharmaceutical tablets. The aim is to investigate\nhow mass transport through the material depends on the microstructure. We\nderive a number of goodness-of-fit measures based on numerically calculated\ndiffusion through the material. These are used in combination with measures\nthat characterize the geometry of the pore structure to assess model fit. The\nmodel is found to fit stationary parts of the material well.\n