Scientific and physics-aware machine learning, and data assimilation
  • Luca Magri
    • Group
    • Collaborations
  • Publications
  • Research
    • Overview
    • Scientific machine learning >
      • Physics-aware machine learning
      • Chaotic time series forecasting
      • Nonlinear model reduction
      • Super-resolution and reconstruction
    • Real-time digital twins and data assimilation >
      • Inferring unknown unknowns: Bias-aware data assimilation
    • Optimization >
      • Bayesian optimisation
      • Chaotic systems
    • Mathematical modelling of multi-physics fluids >
      • Reacting flows and sound
    • Quantum computing and machine learning >
      • Solving nonlinear equations with quantum algorithms
      • Linear methods from quantum mechanics
    • Data and codes
  • Jobs/grants
  • Outreach
    • Research Centre in Data-Driven Engineering
    • Data-driven methods, machine learning and optimization
    • Data-driven Dynamical Systems Analysis
  • Consultancy
  • Teaching
    • University modules
    • Artificial intelligence for engineering
    • Mathematical methods
    • Misc
  • Contact

Collaborations

Physics-aware machine learning and data assimilation
Prof. M. Girolami, University of Cambridge & Alan Turing Institute
Prof. O. Matar, Imperial College London
Prof. K. Duraisamy, University of Michigan
Prof. G. Iaccarino, Stanford University
Dr M. Bothien, Ansaldo Energia Switzerland
Prof. W. Polifke, TU Munich
Prof. A. N. Doan, TU Delft

Optimization and linear flow analysis
Prof. P. Schmid, KAUST

Sensitivity of chaotic reacting flows
Prof. Q. Wang and Nisha Chandramoorthy, MIT
Dr P. Blonigan, Sandia National Labs.
​

Thermoacoustics
Dr. A. Orchini, TU Berlin
Prof. J. Moeck, NTNU
Dr. C. Silva, 
Ansaldo Energia Switzerland


Physics of sound 
Rolls Royce
Dr A. Giusti, Imperial College London
Prof. N. Noiray, ETH Zürich
Prof. S. Hochgreb, University of Cambridge


Simulation and stability of bluff-body wakes
Dr. G. Rigas, Imperial College London
Dr. L. Esclapez, Berkeley


Uncertainty quantification
Dr M. Bauerheim, ISAE-SUPAERO
Prof. F. Nicoud, University of Montpellier
© 2024 Luca Magri
  • Luca Magri
    • Group
    • Collaborations
  • Publications
  • Research
    • Overview
    • Scientific machine learning >
      • Physics-aware machine learning
      • Chaotic time series forecasting
      • Nonlinear model reduction
      • Super-resolution and reconstruction
    • Real-time digital twins and data assimilation >
      • Inferring unknown unknowns: Bias-aware data assimilation
    • Optimization >
      • Bayesian optimisation
      • Chaotic systems
    • Mathematical modelling of multi-physics fluids >
      • Reacting flows and sound
    • Quantum computing and machine learning >
      • Solving nonlinear equations with quantum algorithms
      • Linear methods from quantum mechanics
    • Data and codes
  • Jobs/grants
  • Outreach
    • Research Centre in Data-Driven Engineering
    • Data-driven methods, machine learning and optimization
    • Data-driven Dynamical Systems Analysis
  • Consultancy
  • Teaching
    • University modules
    • Artificial intelligence for engineering
    • Mathematical methods
    • Misc
  • Contact