Deep learning frameworks for structural topology optimization

dc.contributor.advisor	Adarsh Krishnamurthy
dc.contributor.author	Rade, Jaydeep
dc.contributor.department	Electrical and Computer Engineering
dc.date	2021-06-11T00:49:02.000
dc.date.accessioned	2021-08-14T06:34:35Z
dc.date.available	2021-08-14T06:34:35Z
dc.date.copyright	Sat May 01 00:00:00 UTC 2021
dc.date.embargo	2021-04-22
dc.date.issued	2021-01-01
dc.description.abstract	<p>Topology optimization has emerged as a popular approach to refine a component's design and increasing its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single machine learning model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend the current approaches to higher resolutions.</p> <p>In this thesis, we explore deep learning-based frameworks that are consistent with traditional topology optimization algorithms for three-dimensional topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each trying to learn a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better than current ML-based topology optimization methods.</p>
dc.format.mimetype	application/pdf
dc.identifier	archive/lib.dr.iastate.edu/etd/18592/
dc.identifier.articleid	9599
dc.identifier.contextkey	23293968
dc.identifier.doi	https://doi.org/10.31274/etd-20210609-153
dc.identifier.s3bucket	isulib-bepress-aws-west
dc.identifier.submissionpath	etd/18592
dc.identifier.uri	https://dr.lib.iastate.edu/handle/20.500.12876/6wBlnM6r
dc.language.iso	en
dc.source.bitstream	archive/lib.dr.iastate.edu/etd/18592/Rade_iastate_0097M_19248.pdf\|\|\|Fri Jan 14 21:44:17 UTC 2022
dc.subject.keywords	Algorithmically-Consistent Learning
dc.subject.keywords	Deep Learning
dc.subject.keywords	Sequece Models
dc.subject.keywords	Topology Optimization
dc.title	Deep learning frameworks for structural topology optimization
dc.type	article
dc.type.genre	thesis
dspace.entity.type	Publication
relation.isOrgUnitOfPublication	a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.discipline	Electrical Engineering( Communicationsand Signal Processing)
thesis.degree.level	thesis
thesis.degree.name	Master of Science

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