ABSTRACT: Understanding and manipulating fluid flows is foundational to advancing
technologies in the fields of energy, health, and transportation. Modeling these systems is
challenging because they are high-dimensional, turbulent, and nonlinear. With advances in
computational power and measurement technologies, increasing volumes of data are being
collected from fluid simulations and experiments. My interdisciplinary work aims to update
and improve the methods we use for analysis and interpretation of fluid systems using
cutting edge data-driven methods (i.e. machine learning). Data-driven techniques have been
shown to be effective at discovering phenomenon and controlling unsteady fluid flows,
though there are few examples of data-driven methods being used in conjunction with
experiments. In this talk, I will explore how data-driven techniques can be used to study
various fluid systems. By employing modeling and optimization techniques in the loop and
with previously collected experimental data, we are able to better optimize, model, and
control these flows.

BIO: Isabel Scherl is a postdoctoral scholar in Mechanical and Civil Engineering at Caltech
in the Computational and Data-Driven Fluid Dynamics group with Tim Colonius. In
January 2025, she will join the faculty as an Assistant Professor in the Mechanical and
Industrial Engineering Department at the University of Massachusetts Amherst. Her
postdoctoral work uses data assimilation techniques for turbulence modeling. She
completed her PhD at the University of Washington advised by Steven Brunton and Brian
Polagye in mechanical engineering. Her graduate research augmented experimental fluid
mechanics with machine learning. Specifically, she focused on data-driven modeling,
control, and optimization of vertical axis turbine arrays. Before UW, she graduated with a
Bachelor of Science with honors in mechanical engineering from Brown University. Her
continued focus is in applying data-driven methods to pressing challenges in fluid
dynamics.