Physics Informed Neural Networks for Engineering Systems

This thesis explores the application of deep learning techniques to problems in fluid mechanics, with particular focus on physics informed neural networks. Physics

informed neural networks leverage the information gathered over centuries in the

form of physical laws mathematically represented in the form of partial differential

equations to make up for the dearth of data associated with engineering and physi-

cal systems. To demonstrate the capability of physics informed neural networks, an

inverse and a forward problem are considered. The inverse problem involves discov-

ering a spatially varying concentration ?field from the observations of concentration

of a passive scalar. A forward problem involving conjugate heat transfer is solved as

well, where the boundary conditions on velocity and temperature are used to discover

the velocity, pressure and temperature ?fields in the entire domain. The predictions of

the physics informed neural networks are compared against simulated data generated

using OpenFOAM.