Deep learning frameworks for structural topology optimization

dc.contributor.advisor Adarsh Krishnamurthy Rade, Jaydeep
dc.contributor.department Electrical and Computer Engineering 2021-06-11T00:49:02.000 2021-08-14T06:34:35Z 2021-08-14T06:34:35Z Sat May 01 00:00:00 UTC 2021 2021-04-22 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/
dc.identifier.articleid 9599
dc.identifier.contextkey 23293968
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/18592
dc.language.iso en
dc.source.bitstream archive/|||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 Electrical Engineering( Communicationsand Signal Processing) thesis Master of Science
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