Luca Magri

Short CV

Luca is a Reader in data-driven fluid mechanics at Imperial College London, Aeronautics Department. Luca is a Fellow of The Alan Turing Institute, Hans Fischer Fellow of the Institute for Advanced Study (TU Munich) and Visiting Academic Fellow at Cambridge University Engineering Department. Prior to joining Imperial, Luca was a Lecturer at Cambridge University Engineering Department, Royal Academy of Engineering (RAEng) Research Fellow, and Fellow of Pembroke College. Prior to becoming a lecturer and RAEng Research Fellow at Cambridge, he was a postdoctoral Fellow at Stanford University Center for Turbulence Research. He obtained his PhD in Engineering at the University of Cambridge. His research is currently funded by an ERC Starting Grant.

Selected Awards

• 2023, UKRI AI for Net Zero grant
• 2023, EU-Italy Minister of University and Research, Young Researcher
• 2023, Simons Fellow, Isaac Newton Institute
• 2022, Fellow, Royal Aeronautical Society
• 2022, Visiting Scholar Fellow, Stanford University
• 2021, Fellow of The Alan Turing Institute
• 2020, ERC Starting Grant
• 2019, Hinshelwood Prize (The Combustion Institute, British Section)
• 2018, Bernard Lewis Fellowship (The Combustion Institute)
• 2018, Center for Turbulence Research Summer Program Visiting Scholar Fellowship, Stanford University
• 2017, St John’s college, University of Cambridge
• 2015, Center for Turbulence Research Postdoctoral Fellowship, Stanford University
• 2015, Royal Academy of Engineering Research Fellowship
• 2015, Santander Prize, Best PhD thesis 2015, Visiting Fellowship, TU Munich
• 2015, Center for Turbulence Research Summer Program Visiting PhD Student Fellowship, Stanford University
• 2013, American Society of Mechanical Engineers (ASME) International Gas Turbine Institute Scholarship for outstanding doctoral research in gas-turbine technologies
• 2012, Top 25 most read journal articles, Science Direct

Research Interests

Physics-constrained machine learning, Optimization, Chaotic systems, Bayesian data assimilation, Inverse design, Fluid mechanics, Multi-physics

Selected Publications

First-principles machine learning for COVID-19 modeling
Magri, L. & Doan, N. A. K.
SIAM News, (2020), 53(4)
Download: online

First-principles machine learning modeling of COVID-19
Magri, L. & Doan, N. A. K.
Report, Rapid Assistance Modelling in the Pandemic (RAMP) (2020)
Download: preprint

Physics-constrained data-driven methods for unsteady fluids
Magri, L. & Doan, N. A. K.
TU Institute for Advanced Study Annual Reports (2019)
Download: preprint

Linear flow analysis inspired by quantum-mechanics mathematical methods
Magri, L., Schmid, P. J. & Moeck, J. P.
Annual Review of Fluid Mechanics, in preparation.

Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics 
Racca, A & Magri, L.
Neural networks, under review
Download: preprint

Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
Doan, N. A. K., Polifke, W. & Magri, L.
under review
Download: preprint

Auto-Encoded Reservoir Computing  for Turbulence Learning
Doan, N. A. K., Polifke, W. & Magri, L.
Lectures notes on computer science, accepted
Download: preprint

Automatic-differentiated physics-informed echo state network
Racca, A. & Magri, L.
Lectures notes on computer science, accepted
Download: preprint

Using nonlinear optimization to enhance ignition in non-premixed jets
Qadri, U. A., Magri, L., Ihme, M. & Schmid, P. J.
Proceedings of the Royal Society A (2021), doi:10.1098/rspa.2020.0472
Download: preprint, published
 
Compositional and entropic noise generated in subsonic non-isentropic nozzles
De Domenico, F., Rolland, E., Rodrigues, J., Magri, L., & Hochgreb, S.
Journal of Fluid Mechanics (2021), vol. 910, A5.
Download: published (open access)

A data-driven kinematic model of a ducted premixed flame
Yu, H., Juniper, M. P. & Magri, L. 
Proceedings of the Combustion Institute (2021), 38(4), pp. 6231-6239. 
Download: preprint, published

Physics-informed echo state networks
Doan, N. A. K., Polifke, W. & Magri, L.
Journal of Computational Science (2020), vol. 47, 101237.
Download: preprint, published

Degenerate perturbation theory in thermoacoustics: high-order sensitivities and exceptional points
Orchini, A., Magri, L., Silva, C. F., Mensah, G. & Moeck, J. P.
Journal of Fluid Mechanics (2020), vol. 903, A37. 
Download: preprint, published

Learning ergodic averages in chaotic systems
Huhn, F. & Magri, L.
Lecture Notes in Computer Science, LNCS, (2020), vol. 12142, pp. 124-132.
Download: preprint, published

Learning hidden variables in a chaotic system: A physics-informed echo state network approach
Doan, N. A. K., Polifke, W. & Magri, L.
Lecture Notes in Computer Science, LNCS, (2020), vol. 12142, pp. 117-123.
Download: preprint, published

Optimisation of chaotically perturbed acoustic limit cycles
Huhn, F. & Magri, L. 
Nonlinear dynamics (2020), vol. 100, pp. 1641-1657.
Download: preprint, published version

Sensitivity of the Rayleigh criterion in thermoacoustics
Magri, L., Juniper, M. P. & Moeck, J. P.
Journal of Fluid Mechanics (Rapids) (2020), vol. 882, R1.
Download: preprint, published version

Stability, sensitivity and optimisation of chaotic acoustic oscillations
Huhn, F. & Magri, L. 
Journal of Fluid Mechanics (2020), vol. 882, A24. 
Download: preprint, published version

Combined state and parameter estimation in level-set methods  
Yu, H., Juniper, M. & Magri, L.
Journal of Computational Physics (2019), vol. 399, 108950.
Download: preprint, published version.

Data assimilation and optimal calibration in nonlinear models of flame dynamics 
Yu, H., Jaravel, T., Juniper, M., Ihme, M. & Magri, L.
Journal of Engineering for Gas Turbines and Power (2019), 141(12), 121010.
Download: preprint, published version 

Physics-informed echo state networks for chaotic systems forecasting
Doan, N. A. K, Polifke, W. & Magri, L.
Lecture Notes in Computer Science, LNCS, (2019),  vol. 11539, pp. 192-198.
Download: preprint, published version 

Data assimilation in a nonlinear time-delayed dynamical system with Lagrangian optimization
Traverso, T & Magri, L.
Lecture Notes in Computer Science, LNCS, (2019),  vol. 11539, pp. 156-168.
Download: preprint, published version

Adjoint characteristic decomposition of one-dimensional waves
Magri, L.
Journal of Computational Physics (2019), vol. 388, pp. 454-461.
Download: preprint, published version.

Adjoint methods as design tools in thermoacoustics
Magri, L.
Applied Mechanics Reviews (2019), 71(2), 020801.
Download: preprint, published version.