Accelerating optics design optimizations with deep learning

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dc.contributor.author Hegde, Ravi S.
dc.date.accessioned 2019-07-16T09:58:26Z
dc.date.available 2019-07-16T09:58:26Z
dc.date.issued 2019-06
dc.identifier.citation Hegde, Ravi S., �Accelerating optics design optimizations with deep learning�, Optical Engineering, DOI: 10.1117/1.OE.58.6.065103, vol. 58, no. 6, Jun. 2019. en_US
dc.identifier.issn 0091-3286
dc.identifier.uri https://doi.org/10.1117/1.OE.58.6.065103
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/4594
dc.description.abstract We show that design optimizations, an integral but time-consuming component of optical engineering, can be significantly sped-up when paired with deep neural networks (DNNs). By using the DNN indirectly for choosing initializations and candidate preselection, our approach obviates the need for large networks, big datasets, long training epochs, and excessive hyperparameter optimization. For a 16-layered thin-film design problem, our surrogate-assisted differential evolution (DE) algorithm is able to achieve similar optimal solutions as that of an unassisted DE using only 10% of the function evaluation budget. Our approach is a promising option for the optimal design of optical devices and systems.
dc.description.statementofresponsibility by Ravi S. Hegde
dc.format.extent vol. 58, no. 6
dc.language.iso en en_US
dc.publisher Society of Photo-optical Instrumentation Engineers en_US
dc.subject Data modeling en_US
dc.subject Optical design en_US
dc.subject Optical engineering en_US
dc.subject Neurons en_US
dc.subject Performance modeling en_US
dc.subject Reflectivity en_US
dc.subject Thin films en_US
dc.subject Optimization (mathematics) en_US
dc.subject Network architectures en_US
dc.subject Neural networks en_US
dc.title Accelerating optics design optimizations with deep learning en_US
dc.type Article en_US
dc.relation.journal Optical Engineering


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