AERSP 313: Aerospace Analysis

Semesters: Falls of 2019, 2020, 2021, 2022 (Online examples, continuously developing)

This course is designed to reinforce the mathematical concepts learned in the prerequisite mathematics and computer science courses and to present new mathematical material that is necessary for aeronautics, astronautics, dynamics and control, and fluid dynamics analysis. In practice, analytical and numerical approaches to problem-solving are complementary; hence, this course will emphasize a combined analytical and numerical treatment.

Student comment: “What do you call a wizard who is good at calculus? – A mathemagician.”

AERSP/ME 597: Machine Learning for Engineering

Semester: Springs of 2020, 2022, 2024 (slides as of 04/2024)

The course (1) covers the machine learning techniques for the data-driven modeling and data analysis with emphasis on aerospace engineering applications, and (2) exposes the students to the latest advances in the data-driven modeling studies that would be beneficial to their research. Specifically, the course consists of four topics: (a) mathematical background, (b) regression, (c) reduced-order modeling, and (d) data-driven modeling of dynamical systems. These topics are accompanied by case studies representing the application of machine learning techniques in aerospace engineering research. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probability would be helpful.

Since 2024, the course is cross-listed with ME.

Student comment: “It’s given me better appreciation for machine learning and knowledge about how something that was previously a magical black box to me actually works.”

AERSP 597: System Analysis

Semester: Spring of 2024

This course will cover a broad range of mathematical concepts and techniques required for the analysis of engineering systems in the time domain. The objectives are to develop a fundamental understanding of basics of linear algebra, calculus and probability theory and to develop an appreciation for their applications in the study of dynamical systems.

The course is initiated by Dr. Puneet Singla and in collaboration with Dr. Roshan Eapen.

Student comment: “… got to learn some of the things which I only knew superficially in detail.”

AERSP 505: Aero- and Hydro-elasticity

Semester: Springs of 2021, 2023

The course focuses on the modeling and analysis of the interaction between fluid flow and solid structures in the context of aerospace and mechanical engineering. The content starts from basic concepts of dynamical systems and stability analysis, to static aeroelastic analysis (divergence, control reversal, etc.) of airfoils and wings, to dynamic aeroelastic analysis (flutter, coupling to flight dynamics, etc.). The course ends with advanced topics in computational aeroelasticity, multi-disciplinary design optimization, aeroelasticity of special systems (hypersonic vehicles, rotary-wing vehicles, etc.)

Student comment: “The course modules were clearly laid out and the detailing acted as a great introduction to this topic.”

Coming soon: Modal Analysis for Structures and Fluids

Modal analysis has been a standard and fundamental technique for the characterization, understanding, and modeling of dynamical systems, by extracting out physically-important spatial and temporal features. Modal analysis has been extensively applied to mechanical and structural systems, and is also recently becoming common practice in the analysis of, e.g., fluid dynamics. This course aims to present general modal analysis techniques that are applicable to structures, fluids, and other dynamical systems that evolve in a spatiotemporal manner. The course will emphasize on the data-driven aspect of modal analysis, i.e., the processing, analysis, and interpretation of computational and experimental datasets. Examples include proper orthogonal decomposition and dynamic mode decomposition. The course will start with presenting the fundamentals behind these techniques and subsequently extend to the physical interpretation of the underlying dynamical system as well as the construction of predictive reduced-order models. In addition, the course will discuss the numerical implementation of the algorithms, so that the modal analysis techniques can be correctly applied to problems of practical scale.

Computational Science Minor Program

The Computational Sciences minor provides the necessary skills to use computers to study and solve scientific, engineering and data-centric problems across a wide range of disciplines. The minor complements the areas of theory and experimentation found in traditional scientific and engineering studies through the use of computational modeling, algorithm design, and event-driven programming. Students will customize the minor by selecting two advanced courses in their discipline or related areas that build upon the computational foundations provided in prescribed courses. The minor will prepare students with the skills necessary to apply computational methods in a variety of scientific and engineering disciplines.

See our website for details in course requirements and enrollment.