176 citations · 283 across the 28 of their papers we have counts for
17 papers
Data-driven modeling of an oscillating surge wave energy converter using dynamic mode decomposition
Brittany Lydon, Brian Polagye, Steven Brunton
Modeling wave energy converters (WECs) to accurately predict their hydrodynamic behavior has been a challenge for the wave energy field. Often, this results in either low-fidelity,…
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning
Kartik Krishna, Steven L. Brunton, Zhuoyuan Song
Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to compute time-varying analogs of invariant manifolds in unsteady fluid flow fields. These manifolds are useful…
Data-Driven Modeling for Transonic Aeroelastic Analysis
Nicola Fonzi, Steven L. Brunton, Urban Fasel
Aeroelasticity in the transonic regime is challenging because of the strongly nonlinear phenomena involved in the formation of shock waves and flow separation. In this work, we int…
Machine Learning for Partial Differential Equations
Steven L. Brunton, J. Nathan Kutz
Partial differential equations (PDEs) are among the most universal and parsimonious descriptions of natural physical laws, capturing a rich variety of phenomenology and multi-scale…
The transformative potential of machine learning for experiments in fluid mechanics
Ricardo Vinuesa, Steven L. Brunton, Beverley J. McKeon
The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the origin…
Multiscale model reduction for incompressible flows
Jared L. Callaham, Jean-Christophe Loiseau, Steven L. Brunton
Many unsteady flows exhibiting complex dynamics are nevertheless characterized by emergent large-scale coherence in space and time. Reduced-order models based on Galerkin projectio…