Inverse design of anisotropic spinodoid materials with prescribed diffusivity
2022 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 12, no 1, article id 17413
Article in journal (Refereed) Published
Abstract [en]
The three-dimensional microstructure of functional materials determines its effective properties, like the mass transport properties of a porous material. Hence, it is desirable to be able to tune the properties by tuning the microstructure accordingly. In this work, we study a class of spinodoid i.e. spinodal decomposition-like structures with tunable anisotropy, based on Gaussian random fields. These are realistic yet computationally efficient models for bicontinuous porous materials. We use a convolutional neural network for predicting effective diffusivity in all three directions. We demonstrate that by incorporating the predictions of the neural network in an approximate Bayesian computation framework for inverse problems, we can in a computationally efficient manner design microstructures with prescribed diffusivity in all three directions. © 2022, The Author(s).
Place, publisher, year, edition, pages
Nature Research , 2022. Vol. 12, no 1, article id 17413
Keywords [en]
anisotropy, article, convolutional neural network, decomposition, diffusivity, prediction, Bayes theorem, diffusion weighted imaging, normal distribution, porosity, Diffusion Magnetic Resonance Imaging
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:ri:diva-61195DOI: 10.1038/s41598-022-21451-6Scopus ID: 2-s2.0-85140077785OAI: oai:DiVA.org:ri-61195DiVA, id: diva2:1716484
Note
Funding details: 2019-01295; Funding details: Nvidia; Funding text 1: We acknowledge the financial support of the Swedish Research Council for Sustainable Development (Grant Number 2019-01295). A GPU used for part of this research was donated by the NVIDIA Corporation. The computations were in part performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC).; Funding text 2: We acknowledge the financial support of the Swedish Research Council for Sustainable Development (Grant Number 2019-01295). A GPU used for part of this research was donated by the NVIDIA Corporation. The computations were in part performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC).
2022-12-062022-12-062023-05-26Bibliographically approved