University of Cambridge > > Fluids Group Seminar (CUED) > Keep the physics in the model if you can: Data assimilation in Fluid Mechanics

Keep the physics in the model if you can: Data assimilation in Fluid Mechanics

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If you have a question about this talk, please contact Nirmani Rathnayake.

This talk will show how to combine physics-based modelling with data-driven machine learning. The method assimilates data into physics-based models using Bayesian inference accelerated with adjoint methods. In other words, it takes a qualitatively-accurate physics-based model and renders it quantitatively-accurate by assimilating data.

If the physics of the problem is known, this method is better than assimilating data into a Neural Network. This is because the physics-based model requires less training data, is interpretable, and extrapolates to situations that share the same physics. This framework also rigorously compares physics-based models against each other, allowing the best model to be selected.

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

This talk is part of the Fluids Group Seminar (CUED) series.

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