Physics-Informed Deep Learning for Computational Fluid Flow Analysis: Coupling of physics-informed neural networks and autoencoders for aerodynamic flow predictions on variable geometries

The main objective of this thesis was to explore the capabilities of neural networks in terms of representing governing differential equations, primarily in the purview of fluid/aero dynamic flows. The governing differential equations were accommodated within the loss functions for training the neural networks, thereby making them 'physics...