AI in Aeronautical Engineering: Intelligent Methods for Aircraft Design, Simulation and Optimization

Scope and Objectives

This workshop deals with how modern AI methods, especially large language models (LLMs), physics-informed neural networks (PINNs), and other data-driven frameworks, can revolutionize the aircraft engineering and system design lifecycle. The broad and sudden availability of LMs have left a gap between the capability of the AI-tools and their application in the engineering disciplines, which leaves a wide open field of opportunity for domain experts, for research and development into the application of these tools into engineering processes.

We welcome work on both conceptual design and detailed engineering, including integrating AI into computational workflows (e.g., CFD, FEA), design-space exploration, and rapid prototyping. The aim is to highlight tools and networks that embed physical laws and domain knowledge directly within learning architecture Computer for high-fidelity simulation and optimization. In addition, aspects of how these developments interface with aeronautical engineering education and computational AI-infrastructure will be discussed.

The workshop will provide ample possibilities for interaction, discussions and networking, e.g. panel discussion on relevant topics aimed at exchange of ideas.

The workshop is by invitation only. If there is an interest contact one of the organisers.

Topics of Interest

Potential topics include, but are not limited to:

• Physics-Informed & Hybrid Modeling: Embedding physical laws and domain knowledge within AI frameworks to enhance simulation accuracy and efficiency.
• Large Language Models as Design Assistants: AI-tools for automated engineering document generation (requirements,specs, reports), and for concept generation and modelling for system analysis and evaluation.
• Surrogate and Reduced-Order Modeling: Data‑driven metamodels built with modern deep‑learning architectures for design analytics
• Interactive & Generative Design: Deep generative models (VAEs, GANs, diffusion models) for e.g. novel geometries.
• Optimization and control: How will AI tools be integrated in optimization and control of Aeronautical Engineering systems.
• Education: How will the AI tools be integrated in the modern Aeronautical Engineering curriculum.
• Infrastructure: What are the implications of  the modern large scale computation AI-infrastructure for Aeronautical Engineering.

Organizing Committee

• Emilia Villani (ITA, Brazil)
• Dan Henningson (Royal Institute of Technology, Sweden)
• Petter Krus (Linköping University, Sweden)
• Prof Fredrik Heintz (Linköping University, Sweden)
• Lt. Col. Guilherme Moreira (ITA, Brazil)

Programme:

To appear in due time.