Description

This project aims to explore the integration of physics-informed machine learning (PIML) techniques in the analysis and design of aerospace systems. The student will investigate how PIML can be used to enhance the accuracy and efficiency of simulations, particularly in complex aerodynamic, structural, and propulsion systems. The project will involve developing and testing machine learning models that incorporate physical laws and constraints to predict system behavior and optimize design parameters. The outcomes could lead to more reliable and computationally efficient methods for solving high-fidelity aerospace engineering problems.

Key Objectives:

• A review of relevant literature on PIML in aerospace applications.

• Development of a PIML model for a selected aerospace system or component.

• Comparative analysis of the PIML model against traditional methods.