Reconstructing material microstructures using deep learning

dc.contributor.author Singh, Rahul
dc.contributor.department Computer Science
dc.contributor.majorProfessor Chinmay Hegde
dc.contributor.majorProfessor Samik Basu
dc.date 2020-01-07T20:22:20.000
dc.date.accessioned 2020-06-30T01:34:58Z
dc.date.available 2020-06-30T01:34:58Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-01-01
dc.description.abstract <p>Computational materials design integrates targeted materials process-structure and structure-property models in systems frameworks to meet specific engineering needs. The microstructure representations have to satisfy certain statistical parameters to be considered acceptable for further design processes. So, representation of microstructures have to be accurately identified to be considered for materials design. Current techniques have certain limitations in the characterization and reconstruction of these microstructures. The current state-of-the art model-based approaches do not have sufficient parameters that can serve as design variables. The high dimensional nature of this problem relies on dimension reduction that tends to lose important microstructural information. So, in the proposed project we want to design a methodology based on deep adversarial networks to produce these microstructures. The whole framework will be based on generative adversarial networks (GAN) and use them to learn the mapping between latent variables and microstructures. The idea is to train the GAN network to obtain microstructures that are statistically accurate and satisfy certain predefined properties.</p>
dc.format.mimetype PDF
dc.identifier archive/lib.dr.iastate.edu/creativecomponents/429/
dc.identifier.articleid 1410
dc.identifier.contextkey 15339396
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/429
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/16988
dc.source.bitstream archive/lib.dr.iastate.edu/creativecomponents/429/Thesis_MS_Rahul.pdf|||Sat Jan 15 00:14:19 UTC 2022
dc.subject.disciplines Artificial Intelligence and Robotics
dc.subject.keywords Deep Learning
dc.subject.keywords Machine Learning
dc.subject.keywords Generative Adversarial Networks
dc.subject.keywords Material Microstructures
dc.title Reconstructing material microstructures using deep learning
dc.type article
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456
thesis.degree.discipline Computer Science
thesis.degree.level creativecomponent
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