Luca Magri

Luca Magri is a Reader in Data-Driven Fluid Mechanics at Imperial College London's Department of Aeronautics. He holds affiliations as a Fellow of The Alan Turing Institute and a Hans Fischer Fellow at the Technical University of Munich (TUM). His research focuses on physics-constrained machine learning, chaotic systems, and fluid mechanics. Magri’s work bridges computational methods with fluid dynamics, addressing challenges in turbulence, combustion, and acoustics through innovative approaches like reservoir computing and adjoint-based optimization.

Education & Career

• PhD in Engineering, University of Cambridge (2015)
• Postdoctoral Fellow, Stanford University Center for Turbulence Research (2015–2017)
• Lecturer at Cambridge University Engineering Department (2017–2018)
• Current: Reader at Imperial College London since 2018

Research Interests

Magri’s research integrates machine learning with fluid dynamics to model chaotic systems, optimize combustion processes, and analyze turbulence. Key areas include:

• Physics-informed neural networks for extreme event prediction
• Adjoint-based methods for thermoacoustic stability
• Bayesian data assimilation in nonlinear systems

Publications & Awards

His impactful work has been recognized with awards such as the ERC Starting Grant (2019), Royal Aeronautical Society Fellowship (2022), and multiple fellowships from Stanford and Cambridge. Over 50 publications span journals like Journal of Fluid Mechanics and Proceedings of the Royal Society A.

Grants & Collaborations

Magri leads projects funded by the EU Horizon 2020, UKRI, and the ERC. He collaborates globally, including with TUM’s Institute for Advanced Study and Cambridge’s Engineering Department.