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)
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