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Conference Paper: Learning network for laser absorption imaging in flames using mid-fidelity simulations

TitleLearning network for laser absorption imaging in flames using mid-fidelity simulations
Authors
Wei, ChuyuSchwarm, Kevin K.Pineda, Daniel I.Spearrin, R. Mitchell
Issue Date2021
Citation
Optics Infobase Conference Papers, 2021, article no. CTh5A.6 How to Cite?
AbstractA deep neural network is trained using mid-fidelity reacting flow simulations to assist laser absorption imaging of species and temperature in flames with sparse view angles. The method is compared to linear tomography.
Persistent Identifierhttp://hdl.handle.net/10722/365763

 

DC FieldValueLanguage
dc.contributor.authorWei, Chuyu-
dc.contributor.authorSchwarm, Kevin K.-
dc.contributor.authorPineda, Daniel I.-
dc.contributor.authorSpearrin, R. Mitchell-
dc.date.accessioned2025-11-05T09:47:14Z-
dc.date.available2025-11-05T09:47:14Z-
dc.date.issued2021-
dc.identifier.citationOptics Infobase Conference Papers, 2021, article no. CTh5A.6-
dc.identifier.urihttp://hdl.handle.net/10722/365763-
dc.description.abstractA deep neural network is trained using mid-fidelity reacting flow simulations to assist laser absorption imaging of species and temperature in flames with sparse view angles. The method is compared to linear tomography.-
dc.languageeng-
dc.relation.ispartofOptics Infobase Conference Papers-
dc.titleLearning network for laser absorption imaging in flames using mid-fidelity simulations-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-85119525858-
dc.identifier.spagearticle no. CTh5A.6-
dc.identifier.epagearticle no. CTh5A.6-
dc.identifier.eissn2162-2701-

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