alexandroskon Goto Github PK

I am EPSRC research fellow in fluid dynamics and applied mathematics at Cambridge University Engineering Department.

I am currently working on Bayesian inverse problems in fluid dynamics for magnetic resonance velocimetry. In the past, I have worked on aerodynamic modelling, fluid-structure interaction, simulations of ice accretion on aircraft wings, and aerodynamic shape optimisation.

My main research concerns the formulation of new machine learning methods that automatically reconstruct corrupted flowfields. These methods learn the most probable simulation that corresponds to the corrupted flowfield, and, at the same time, infer unknown quantities (e.g. pressure) that are either hard or impossible to measure otherwise.

In a nutshell, I develop algorithms that learn the most probable physical model (aka digital/physical twin) of the flowfield dynamics from data.