{"title":"双相钢中实验观察到的三维微观结构与 GAN 生成的三维微观结构的比较研究","authors":"Ikumu Watanabe, Keiya Sugiura, Ta-Te Chen, Toshio Ogawa, Yoshitaka Adachi","doi":"10.1080/14686996.2024.2388501","DOIUrl":null,"url":null,"abstract":"In a deep-learning-based algorithm, generative adversarial networks can generate images similar to an input. Using this algorithm, an artificial three-dimensional (3D) microstructure can be reprodu...","PeriodicalId":21588,"journal":{"name":"Science and Technology of Advanced Materials","volume":"28 1","pages":""},"PeriodicalIF":7.4000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative study of the experimentally observed and GAN-generated 3D microstructures in dual-phase steels\",\"authors\":\"Ikumu Watanabe, Keiya Sugiura, Ta-Te Chen, Toshio Ogawa, Yoshitaka Adachi\",\"doi\":\"10.1080/14686996.2024.2388501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In a deep-learning-based algorithm, generative adversarial networks can generate images similar to an input. Using this algorithm, an artificial three-dimensional (3D) microstructure can be reprodu...\",\"PeriodicalId\":21588,\"journal\":{\"name\":\"Science and Technology of Advanced Materials\",\"volume\":\"28 1\",\"pages\":\"\"},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science and Technology of Advanced Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1080/14686996.2024.2388501\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science and Technology of Advanced Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1080/14686996.2024.2388501","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Comparative study of the experimentally observed and GAN-generated 3D microstructures in dual-phase steels
In a deep-learning-based algorithm, generative adversarial networks can generate images similar to an input. Using this algorithm, an artificial three-dimensional (3D) microstructure can be reprodu...
期刊介绍：
Science and Technology of Advanced Materials (STAM) is a leading open access, international journal for outstanding research articles across all aspects of materials science. Our audience is the international community across the disciplines of materials science, physics, chemistry, biology as well as engineering.
The journal covers a broad spectrum of topics including functional and structural materials, synthesis and processing, theoretical analyses, characterization and properties of materials. Emphasis is placed on the interdisciplinary nature of materials science and issues at the forefront of the field, such as energy and environmental issues, as well as medical and bioengineering applications.
Of particular interest are research papers on the following topics:
Materials informatics and materials genomics
Materials for 3D printing and additive manufacturing
Nanostructured/nanoscale materials and nanodevices
Bio-inspired, biomedical, and biological materials; nanomedicine, and novel technologies for clinical and medical applications
Materials for energy and environment, next-generation photovoltaics, and green technologies
Advanced structural materials, materials for extreme conditions.
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