128 citations · 725 across the 30 of their papers we have counts for
31 papers
Observing a Dynamical Skeleton of Turbulence in Taylor-Couette Flow Experiments
Christopher J. Crowley, Joshua L. Pughe-Sanford, Wesley Toler +2
Recent work suggests unstable recurrent solutions of the equations governing fluid flow can play an important role in structuring the dynamics of turbulence. Here we present a meth…
Data-driven detection of drifting system parameters
Logan M. Kageorge, Roman O. Grigoriev, Michael F. Schatz
Data taken from observations of the natural world or laboratory measurements often depend on parameters which can vary in unexpected ways. In this paper we demonstrate how machine…
Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
Patrick A. K. Reinbold, Logan M. Kageorge, Michael F. Schatz +1
Machine learning offers an intriguing alternative to first-principles analysis for discovering new physics from experimental data. However, to date, purely data-driven methods have…
Capturing Turbulent Dynamics and Statistics in Experiments with Unstable Periodic Orbits
Balachandra Suri, Logan Kageorge, Roman O. Grigoriev +1
In laboratory studies and numerical simulations, we observe clear signatures of unstable time-periodic solutions in a moderately turbulent quasi-two-dimensional flow. We validate t…
A novel subcritical transition to turbulence in Taylor-Couette flow with counter-rotating cylinders
Christopher J. Crowley, Michael C. Krygier, Daniel Borrero-Echeverry +2
The transition to turbulence in Taylor-Couette flow often occurs via a sequence of supercritical bifurcations to progressively more complex, yet stable, flows. We describe a subcri…
Heteroclinic and Homoclinic Connections in a Kolmogorov-Like Flow
Balachandra Suri, Ravi Kumar Pallantla, Michael F. Schatz +1
Recent studies suggest that unstable recurrent solutions of the Navier-Stokes equation provide new insights into dynamics of turbulent flows. In this study, we compute an extensive…