Dr. Ricardo Vinuesa is the Associate Chair for Research and an Associate Professor at the Department of Aerospace Engineering, University of Michigan. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain), and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand, control and predict complex wall-bounded turbulent flows, such as the boundary layers developing around wings and the flow in urban environments. Dr. Vinuesa has received, among others, an ERC Consolidator Grant, the Harleman Lecture Award, the TSFP Kasagi Award, the MST Emerging Leaders Award, the Goran Gustafsson Award for Young Researchers, the IIT Outstanding Young Alumnus Award and the SARES Young Researcher Award. He received the Outstanding Reviewer Prize of the Journal of Fluid Mechanics and he is also a member of the Young Academy of Science of Spain.

Day 1: Wednesday, September 9th | 14:00 – 15:30

Room: Opus

Title:  The AI/ML Ecosystem for Engineering Analysis, Design, and Control

Moderator: Prof. Juan Alonso (Stanford University, USA)

Panelists:
Ass. Prof. Michael Bauerheim (ISAE-Supaero, France), Prof. Xiaomo Jiang  (Dalian University of Technology, China), Ass. Prof. Ricardo Vinuesa (University of Michigan, USA)

Abstract:
As data availability (from simulation, test, and operations) becomes more widely available, AI/ML-based methods for the analysis, design, and control of aerospace systems, including power and propulsion systems, are becoming more pervasive. But the pace of development is frenetic and the landscape of physics-informed and agentic AI/ML is changing more rapidly than we can keep up with. In this panel we tackle both the current state and 1-2-year predictions for the capability, accuracy, and cost for such methods. What are the most logical applications in engineering in the near term? What transformational capabilities do we expect in the next 5 years? What are the key bottlenecks to make this technology work in realistic industrial environments?