590 citations · 1.8k across the 105 of their papers we have counts for
11 papers · 2 filters
Data-driven unsteady aeroelastic modeling for control
Michelle Hickner, Urban Fasel, Aditya G. Nair +2
Aeroelastic structures, from insect wings to wind turbine blades, experience transient unsteady aerodynamic loads that are coupled to their motion. Effective real-time control of f…
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:…
Dynamic Mode Decomposition for Aero-Optic Wavefront Characterization
Shervin Sahba, Diya Sashidhar, Christopher C. Wilcox +3
Aero-optical beam control relies on the development of low-latency forecasting techniques to quickly predict wavefronts aberrated by the Turbulent Boundary Layer (TBL) around an ai…
Enhancing Computational Fluid Dynamics with Machine Learning
Ricardo Vinuesa, Steven L. Brunton
Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. In this Perspecti…
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…
On the role of nonlinear correlations in reduced-order modeling
Jared L. Callaham, Steven L. Brunton, Jean-Christophe Loiseau
A major goal for reduced-order models of unsteady fluid flows is to uncover and exploit latent low-dimensional structure. Proper orthogonal decomposition (POD) provides an energy-o…