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

Imperial College London (as of 2022)


​Lecturer
  • 2021 - present, Module leader and lecturer, Artificial Intelligence for Aerospace Engineers ​(advanced computational methods for 3rd-4th year, MSc)

University of Cambridge (until 2021)


Lecturer
  • 2019 - 2021, Module leader and lecturer, Mathematical Methods (3M1, 3rd-year)
  • 2019 - 2021, Module leader and lecturer, Fluid Mechanics II (3A3, double module, 3rd year)
  • 2019 - 2021, Coordinator, Heat Transfer laboratory
  • 2018 - 2021, Lecturer, MPhil in Energy Technologies, Gas Turbines.

Supervisor
  • Mathematics Part IB (2nd year, 2P7, Vector calculus, Probability, Linear Algebra)
  • Fluid mechanics I (3rd-year, 3A1, double module, Incompressible fluid mechanics)
  • Fluid mechanics II (3rd-year, 3A3, double module, Compressible fluid mechanics)
  • Mathematical methods (3rd-year, 3M1, Linear algebra, Stochastic processes, Optimization)
© 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