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Physics Informed Neural Networks for Engineering Systems

<div>This thesis explores the application of deep learning techniques to problems in fluid mechanics, with particular focus on physics informed neural networks. Physics</div><div>informed neural networks leverage the information gathered over centuries in the</div><div>form of physical laws mathematically represented in the form of partial differential</div><div>equations to make up for the dearth of data associated with engineering and physi-</div><div>cal systems. To demonstrate the capability of physics informed neural networks, an</div><div>inverse and a forward problem are considered. The inverse problem involves discov-</div><div>ering a spatially varying concentration ?field from the observations of concentration</div><div>of a passive scalar. A forward problem involving conjugate heat transfer is solved as</div><div>well, where the boundary conditions on velocity and temperature are used to discover</div><div>the velocity, pressure and temperature ?fields in the entire domain. The predictions of</div><div>the physics informed neural networks are compared against simulated data generated</div><div>using OpenFOAM.</div>

  1. 10.25394/pgs.12290741.v1
Identiferoai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12290741
Date13 May 2020
CreatorsSukirt (8828960)
Source SetsPurdue University
Detected LanguageEnglish
TypeText, Thesis
RightsCC BY 4.0
Relationhttps://figshare.com/articles/Physics_Informed_Neural_Networks_for_Engineering_Systems/12290741

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