OVERVIEW

This course is designed to provide a comprehensive introduction to the application of reduced-order model (ROM) techniques in aerospace engineering, focusing on aircraft performance and aerodynamic load analysis. The ROM techniques are classified into two categories: data-driven methods based on machine learning (ML); and equations-derived methods based on nonlinear projections (NP). The course aims to equip students with a foundational understanding of ML and NP concepts and practical skills for implementing these approaches in aerospace contexts. By the end of the course, participants will gain knowledge of cutting-edge techniques and their relevance to complex aerospace problems, particularly in predicting steady and unsteady aerodynamic loads, and performing aeroelastic analysis.

The course begins with an introduction to core aerospace engineering problems, focusing on aircraft performance and the complexities involved in aerodynamic and aeroelastic load prediction. There is an overview of steady-state and unsteady aerodynamic modeling approaches. These sessions cover critical topics such as the aerodynamic characteristics of steady and transient flows around aircraft, which are essential for understanding the needs of ROM approaches to accelerate the rate of the design process.

A significant portion of the course is devoted to ROM techniques, which simplify complex aerodynamic and aeroelastic problems into manageable computational tasks. Students will learn about two primary types of ROMs: data-driven ROMs that leverage machine learning algorithms and equations-derived ROMs that rely on projection-based methods. The data-driven ROM segment introduces machine learning techniques such as autoencoders and graph neural networks, highlighting their advantages in handling large datasets with minimal computational cost. Meanwhile, the projection-based ROM session covers techniques such as NP of the residual of the coupled system, which reduces computational complexity without compromising accuracy.

Throughout the course, real-world aerospace applications are emphasized to ensure that participants develop skills that are directly transferable to industry settings. By blending ML techniques with aerospace engineering fundamentals, this course provides a unique perspective on modern aerospace challenges and prepares participants to apply innovative solutions in their careers.

LEARNING OBJECTIVES

• Define key aerospace engineering problems related to aircraft development.
• Recognize the complexities in predicting steady-state and unsteady aerodynamic loads.
• Explain the use of ROMs in complex aerospace computational problems.
• Understand the foundational concepts of ML and NP and their relevance to aerospace engineering.
• Differentiate between data-driven ROMs and equations-derived ROMs.
• Discuss the application of ML techniques like autoencoders and graph neural networks to develop data-driven ROMs.
• Discuss implementation aspects related to projection-based ROMs.
• Assess the impact of integrating ROM techniques in real-world aerospace applications, demonstrating practical problem-solving skills.
• Critically assess the benefits and limitations of using ML and projection methods in aerospace contexts for aircraft load analysis.

AUDIENCE
This course is designed for students and researchers in academia and industry who are focused on advanced topics in aerospace engineering, particularly in aerodynamic load prediction and reduced-order modeling. It also caters to technical decision-makers who seek to understand emerging machine learning and projection-based techniques for future development strategies. The content is tailored to equip participants with both foundational knowledge and practical skills, ensuring they can apply modern machine learning and reduced-order model techniques to real-world aerospace challenges and make informed decisions in research and industrial settings.

COURSE FEES (Sign-In To Register)
- AIAA Member Price: $1195 USD
- Non-Member Price: $1395 USD
- AIAA Student Member Price: $595 USD

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