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SUMMARY:Keep the physics in the model if you can: Data assimilation in Flu
 id Mechanics - Matthew Juniper (University of Cambridge)
DTSTART:20221028T114500Z
DTEND:20221028T124500Z
UID:TALK183479@talks.cam.ac.uk
CONTACT:Nirmani Rathnayake
DESCRIPTION:This talk will show how to combine physics-based modelling wit
 h data-driven machine learning. The method assimilates data into physics-b
 ased models using Bayesian inference accelerated with adjoint methods. In 
 other words\, it takes a qualitatively-accurate physics-based model and re
 nders it quantitatively-accurate by assimilating data. \n\nIf the physics 
 of the problem is known\, this method is better than assimilating data int
 o a Neural Network. This is because the physics-based model requires less 
 training data\, is interpretable\, and extrapolates to situations that sha
 re the same physics. This framework also rigorously compares physics-based
  models against each other\, allowing the best model to be selected. \n\nT
 his work is inspired by David MacKay's book on information theory\, infere
 nce\, and learning algorithms (https://www.inference.org.uk/itprnn/book.pd
 f). I will present applications in Magnetic Resonance Imaging of flows (fl
 ow-MRI) and thermoacoustic oscillations in rockets and aicraft engines. An
  overview of the talk can be found at https://mpj1001.user.srcf.net/MJ_inf
 erence.html.
LOCATION:LR12
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