Matthew Juniper- LIFD colloquium

  • Date:
  • Time: 1pm
  • Location: William Bragg Building

Event Details

Professor Matthew Juniper will be giving a talk on Wednesday 29th March 2023.

  • There will be a catered lunch provided William Bragg SR (GR.25) – 1pm-2pm
  • The talk will be held in William Bragg LT (2.37) 2pm-3pm

Professor Matthew Juniper. Keep the physics in the model if you can: Data assimilation in Fluid Mechanics

This talk will show how to assimilate data rigorously into physics-based models using Bayesian inference accelerated with adjoint methods. Adjoint methods are crucial because they provide the derivatives of the model’s predictions with respect to the model’s parameters. This (i) accelerates data assimilation and (ii) allows uncertainties to be propagated through the model such that posterior uncertainties in parameters are calculated by combining prior uncertainties with measurement uncertainties. Quantification of the uncertainties allows candidate physics-based models to be compared rigorously against each other, allowing the best model to be selected.

If the physics of the problem is known, this method is almost certainly better than assimilating data into a Neural Network because physics-based models require less training data, are interpretable, and can extrapolate to situations that share the same physics.

This work is inspired by David MacKay’s book on information theory, inference, and learning algorithms ( I will present applications in Magnetic Resonance Imaging of flows (flow-MRI) and thermoacoustic oscillations in rockets and aicraft engines. An overview of the talk can be found at

Professor Matthew Juniper Bio

Matthew Juniper is Professor of Thermofluid Dynamics at the Engineering Department of Cambridge University. He completed his PhD from Ecole Central Paris in 2001 and was appointed as Lecturer at the University of Cambridge in 2003. His research interests are in the broad area of hydrodynamic and thermoacoustic instability, adjoint-based sensitivity analysis, shape optimization, and physics-based statistical learning. He is an Associate Editor of the Journal of Fluid Mechanics and has held visiting fellowships/professorships at Ecole Central Lyon, the Institute for Advanced Studies at TU Munich, KTH/Nordita Stokholm, IIT Madras, and the Center for Turbulence Research Summer Program at Stanford University.