<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12290741 |
| Date | 13 May 2020 |
| Creators | Sukirt (8828960) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Physics_Informed_Neural_Networks_for_Engineering_Systems/12290741 |
Page generated in 0.0025 seconds