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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