Using Deep Neural Networks to Predict Material Types in Conditional Point Sampling Applied to Markovian Mixture Models
Year: 2021
Type: conference-paper
Abstract: Conditional Point Sampling (CoPS) is a recently developed stochastic media transport algorithm that has demonstrated a high degree of accuracy in 1-D and 3-D calculations for binary mixtures with Markovian mixing statistics.In theory, CoPS has the capacity to be accurate for material structures beyond just those with Markovian statistics.However, realizing this capability will require development of conditional probability functions (CPFs) that are based not on explicit Markovian properties, but instead are based on latent properties extracted from material structures.Here, we describe a first step towards extracting these properties by developing CPFs using deep neural networks (DNNs).This new approach lays the groundwork for enabling accurate transport on many classes of stochastic media.We train DNNs on ternary stochastic media with Markovian mixing statistics and compare their CPF predictions to those made by existing CoPS CPFs derived based on Markovian mixing properties.We find that the DNN CPF predictions usually outperform the existing approximate CPF predictions, but with wider variance.In addition, even when trained on only one material volume realization, the DNN CPFs are shown to make accurate predictions on other realizations that have the same internal mixing behavior.Our results show that it is possible to form a useful CoPS CPF by using a DNN to extract correlation properties from realizations of stochastically mixed media, thus establishing a foundation for creating CPFs for mixtures other than those with Markovian mixing, where deriving an acurate analytical CPF may not be possible.
DOI: 10.13182/xyz-33784
Authors: Warren Davis, Aaron Olson, Gabriel Popoola, Dan Bolintineanu, Theron Rodgers, Emily Vu
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4287340684