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SUMMARY:Inverse problems in fluid dynamics for enhanced velocimetry - Alex
 andros Kontogiannis (University of Cambridge)
DTSTART:20230216T113000Z
DTEND:20230216T123000Z
UID:TALK195559@talks.cam.ac.uk
CONTACT:Catherine Pearson
DESCRIPTION:We formulate a digital twin approach to the reconstruction of 
 noisy and sparse velocity images. The method learns the most probable flui
 d dynamics model that fits the data by solving a Bayesian inverse Navier
 –Stokes boundary value problem. This jointly reconstructs and segments t
 he velocity field\, and at the same time infers hidden quantities such as 
 the hydrodynamic pressure and the wall shear stress. Using a Bayesian fram
 ework\, we regularize the problem by introducing a priori information abou
 t the unknown parameters in the form of Gaussian random fields. This furth
 er allows us to estimate the uncertainties of the unknowns by approximatin
 g their posterior covariance with a quasi-Newton method. Although this met
 hod has been developed for magnetic resonance velocimetry (flow-MRI)\, it 
 extends to other velocimetry methods such as ultrasound Doppler velocimetr
 y\, particle image velocimetry (PIV) and scalar image velocimetry (SIV).
LOCATION:Open Plan Area\, Institute for Energy and Environmental Flows\, M
 adingley Rise CB3 0EZ
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