115 citations · 379 across the 31 of their papers we have counts for
19 papers · 1 filter
Applying Machine Learning to Study Fluid Mechanics
Steven L. Brunton
This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages:…
Data-driven Modeling of Two-Dimensional Detonation Wave Fronts
Ariana Mendible, Weston Lowrie, Steven L. Brunton +1
Historical experimental testing of high-altitude nuclear explosions (HANEs) are known to cause severe and detrimental effects to radio frequency signals and communications infrastr…
An empirical mean-field model of symmetry-breaking in a turbulent wake
Jared L. Callaham, Georgios Rigas, Jean-Christophe Loiseau +1
This work develops a low-dimensional nonlinear stochastic model of symmetry-breaking coherent structures from experimental measurements of a turbulent axisymmetric bluff body wake.…
Promoting global stability in data-driven models of quadratic nonlinear dynamics
Alan A. Kaptanoglu, Jared L. Callaham, Christopher J. Hansen +2
Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of reduced-order models for a variety of scientific and engineering tasks. However, it is…
Improved approximations to the Wagner function using sparse identification of nonlinear dynamics
Scott T. M. Dawson, Steven L. Brunton
The Wagner function in classical unsteady aerodynamic theory represents the response in lift on an airfoil that is subject to a sudden change in conditions. While it plays a fundam…
Data-driven resolvent analysis
Benjamin Herrmann, Peter J. Baddoo, Richard Semaan +2
Resolvent analysis identifies the most responsive forcings and most receptive states of a dynamical system, in an input--output sense, based on its governing equations. Interest in…