That’s a vortex (probably)!

There are many examples in fluid dynamics in which we wish to use a limited amount of information about a flow (e.g., from sensors) to infer its larger behavior. Rather than treat this inverse problem deterministically, it is valuable to embrace its uncertainty, e.g., due to noisy sensor measurements, and work in a probabilistic setting. Even if we think we have non-noisy measurements, we can still learn a lot about the estimated flow by treating it in this setting: Is the estimate unique? What information is available (and not available) in the sensors? In this talk, I will review some basic tools from Bayesian inference and sequential estimation, and demonstrate their application in a vortex estimation problem. Since most of the challenge of applying these tools arises from the non-linearity of fluid dynamic problems, I will devote attention to the techniques we use to overcome those challenges.

Speaker: Jeff Eldredge is Professor of Mechanical & Aerospace Engineering at the University of California, Los Angeles, where he has served on the faculty since 2003. Prior to this, he received his Ph.D. from Caltech, followed by post-doctoral research at Cambridge University. His research interests lie in computational and theoretical studies of fluid dynamics, including numerical simulation and low-order modeling of unsteady aerodynamics; investigations of aquatic and aerial locomotion in biological and bioinspired systems; and investigations of biomedical and biomedical device flows. He is the author of numerous papers, as well as the book Mathematical Modeling of Unsteady Inviscid Flows. He is a Fellow of the American Physical Society, an Associate Fellow of AIAA, and a recipient of the NSF CAREER award. He has served on the Editorial Board of Physical Review Fluids and as an Associate Editor for the journal Theoretical and Computational Fluid Dynamics.