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. 2019 Dec 31;9:20387. doi: 10.1038/s41598-019-56309-x

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Improving CNN prediction by preprocessing images of porous structures. (a,b) Images of the regular (a) and processed (b) porous structure by removing the trapped pore space and dead-end transport pathways. The blue (red) lines on denote the inlet (outlet) of the molecule transport through the porous structure. The green lines denote the periodic boundaries. (c,d) The effective diffusivity predicted by the CNN model using the preprocessed image of porous structures (c) and the distribution of their absolute and truncated relative errors (d).