Project 4

With the increasing complexity of modern space structures, design, optimization, and performance analysis using physics-based finite element methods require significant computational resources. Using the rapid developments in Artificial Intelligence (AI) and Machine Learning (ML), this project aims to integrate these advances into the analysis of complex space structures to provide greater accuracy, efficiency, and adaptability. In this project, deep learning techniques will be employed to model the behavior of real-world space structures, such as the International Space Station, under the ubiquitous presence of material uncertainty to demonstrate the advantageous effects of AI, ML, and data-driven approaches for space applications.

The student will learn to use AI and ML codes developed at the lab to perform the computation and finally how to use data from real-world space structures. They will also write reports and prepare presentations based on their research. This training estimated at 10 hrs/week over two semesters and will help the student acquire necessary skill sets early in their academic journey that will provide them with a competitive edge in their future career.

The students will participate in research group meetings and present their findings at regular intervals improving their scientific  communication skills. They will also present their findings at the Student Research Symposium organized by the Arizona NASA Space Grant Consortium (AZSGC).