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dc.contributor.authorPawar, Suraj
dc.contributor.authorSan, Omer
dc.contributor.authorAksoylu, Burak
dc.contributor.authorRasheed, Adil
dc.contributor.authorKvamsdal, Trond
dc.date.accessioned2021-02-18T07:45:45Z
dc.date.available2021-02-18T07:45:45Z
dc.date.created2020-12-26T10:55:55Z
dc.date.issued2021
dc.identifier.issn1070-6631
dc.identifier.urihttps://hdl.handle.net/11250/2728798
dc.description.abstractRecent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.en_US
dc.language.isoengen_US
dc.publisherAmerican Institute of Physics, American Physical Society, Division of Fluid Dynamicsen_US
dc.titlePhysics guided machine learning using simplified theoriesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume33en_US
dc.source.journalPhysics of Fluidsen_US
dc.source.issue1en_US
dc.identifier.doi10.1063/5.0038929
dc.identifier.cristin1863258
dc.description.localcodeLocked until 8.1.2022 due to copyright restrictions. Published by AIP Publishing. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The following article appeared in Physics of Fluids and may be found at http://dx.doi.org/10.1063/5.0038929en_US
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