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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,12,30]]},"abstract":"Abstract<\/jats:title>\n An approach for deploying stochastic three-dimensional (3D) models to generate microstructural 3D image data for training super-resolution networks is investigated for three different scaling factors \n \n \n \n \u03b1<\/mml:mi>\n \u2208<\/mml:mo>\n {<\/mml:mo>\n 2<\/mml:mn>\n ,<\/mml:mo>\n 4<\/mml:mn>\n ,<\/mml:mo>\n 8<\/mml:mn>\n }<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>. The presented approach addresses the issue of scarcity in training data by training the networks only on artificial image data, generated by means of a stochastic 3D model that produces digital twins of the nanoporous inner structure of active particles in battery cathodes. In addition, the performance of super-resolution networks is investigated when complementing the input data, i.e. low-resolved microstructural 3D image data, with spatially resolved transport simulations. The performance of the trained networks is evaluated based on real tomographic image data, and quantified with respect to various geometric descriptors and effective transport properties. It turned out that the integration of transport simulations into the training of super-resolution networks showed an increase in performance for the scaling factors \n \n \n \n \u03b1<\/mml:mi>\n \u2208<\/mml:mo>\n {<\/mml:mo>\n 2<\/mml:mn>\n ,<\/mml:mo>\n 4<\/mml:mn>\n }<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>, but a decrease in performance for \u03b1<\/jats:italic>\u2009=\u20098. However, training the networks on artificial image data was effective in all cases.<\/jats:p>","DOI":"10.1088\/2632-2153\/ae0c55","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T22:53:00Z","timestamp":1758927180000},"page":"045006","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Super-resolving 3D nanostructures using artificially generated image data and spatial transport simulations"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1624-6542","authenticated-orcid":true,"given":"Orkun","family":"Furat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7481-2966","authenticated-orcid":true,"given":"Phillip","family":"Gr\u00e4fensteiner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9930-8912","authenticated-orcid":true,"given":"Rishabh","family":"Saxena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-627X","authenticated-orcid":true,"given":"Markus","family":"Osenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5782-6773","authenticated-orcid":true,"given":"Matthias","family":"Neumann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ingo","family":"Manke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0503-4555","authenticated-orcid":true,"given":"Thomas","family":"Carraro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Volker","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"year":"2002","author":"Torquato","key":"mlstae0c55bib1","type":"book"},{"key":"mlstae0c55bib2","doi-asserted-by":"publisher","first-page":"745","DOI":"10.3390\/s22030745","type":"journal-article","article-title":"Comparison of DEM super-resolution methods based on interpolation and neural networks","volume":"22","author":"Zhang","year":"2022","journal-title":"Sensors"},{"key":"mlstae0c55bib3","doi-asserted-by":"publisher","DOI":"10.1016\/j.acags.2023.100143","type":"journal-article","article-title":"A comparative analysis of super-resolution techniques for enhancing micro-CT images of carbonate rocks","volume":"20","author":"Soltanmohammadi","year":"2023","journal-title":"Applied Computing and Geosciences"},{"year":"2013","author":"Chiu","key":"mlstae0c55bib4","type":"book"},{"year":"2021","author":"Jeulin","key":"mlstae0c55bib5","type":"book"},{"key":"mlstae0c55bib6","doi-asserted-by":"publisher","first-page":"5225","DOI":"10.1021\/acsenergylett.4c01931","type":"journal-article","article-title":"Digital twin battery modeling and simulations: A new analysis and design tool for rechargeable batteries","volume":"9","author":"Kim","year":"2024","journal-title":"ACS Energy Lett."},{"key":"mlstae0c55bib7","doi-asserted-by":"publisher","DOI":"10.3389\/fmats.2022.818535","type":"journal-article","article-title":"Digital twins for materials","volume":"9","author":"Kalidindi","year":"2022","journal-title":"Frontiers in Materials"},{"key":"mlstae0c55bib8","first-page":"pp 243","type":"book","article-title":"Simulation of microstructures and machine learning","author":"Schladitz","year":"2024"},{"key":"mlstae0c55bib9","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1111\/jmi.12944","type":"journal-article","article-title":"Reconstruction of highly porous structures from FIB-SEM using a deep neural network trained on synthetic images","volume":"281","author":"Fend","year":"2021","journal-title":"J. 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