Session S37 - New Developments in Mathematical Fluid Dynamics

Friday, July 16, 14:30 ~ 14:55 UTC-3

A Bayesian Approach to Estimating Background Flows from a Passive Scalar

Justin Krometis

Virginia Tech, United States   -   This email address is being protected from spambots. You need JavaScript enabled to view it.

We consider the Bayesian inverse problem of estimating a background flow field from the partial and noisy observation of a passive scalar (e.g., a solute concentration) governed by advection and diffusion. We provide conditions under which the inference is consistent, i.e., the posterior converges to a Dirac measure on the true flow as the number of observations grows large. We also attack the problem computationally by leveraging MCMC methods adapted in recent years to infinite-dimensional settings.

Joint work with Nathan Glatt-Holtz (Tulane University, United States) and Jeff Borggaard (Virginia Tech, United States).

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