Steve Brunton

Associate Professor

University of Washington

United States

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Steve Brunton is an Associate Professor at the University of Washington, USA. His research interests encompass reduced-order modeling, machine learning applications in fluid dynamics, and the analysis of spatiotemporal brain dynamics. Recent publications include works on hydrodynamic pattern formation, data-driven modeling of wave energy converters, and advanced techniques for detecting coherent structures in fluid flows.

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Collaborators (8)

Georgios Rigas

Senior Lecturer

Imperial College London

UNITED KINGDOM

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Alan Kaptanoglu

Assistant Professor of Mathematics

New York University

UNITED STATES

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Beverley McKeon

Professor

Stanford University

UNITED STATES

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Arka Majumdar

University of Washington

UNITED STATES

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C. Hansen

Columbia University

UNITED STATES

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M. Landreman

UNITED STATES

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Wolfgang Schröder

RWTH Aachen University

GERMANY

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Jin-Moo Lee

Professor

Washington University School of Medicine

UNITED STATES

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Steve Brunton

Research Interests

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About

Articles (28)

Arousal as a universal embedding for spatiotemporal brain dynamics

Reduced-order modeling and classification of hydrodynamic pattern formation in gravure printing

Separation of periodic orbits in the delay-embedded space of chaotic attractors

A deep learning approach to wall-shear stress quantification: from numerical training to zero-shot experimental application

Data-driven modeling of an oscillating surge wave energy converter using dynamic mode decomposition

Leveraging three-dimensionality for navigation in bluff-body wakes

Towards extending the aircraft flight envelope by mitigating transonic airfoil buffet

Promising directions of machine learning for partial differential equations

Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements

Constrained Optimization of Sensor Placement for Nuclear Digital Twins

PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator

Multi-fidelity reduced-order surrogate modelling

Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics

Lagrangian gradient regression for the detection of coherent structures from sparse trajectory data

Nonlinear parametric models of viscoelastic fluid flows

Physics-informed dynamic mode decomposition

Large area optimization of meta-lens via data-free machine learning

Discovering governing equations from partial measurements with deep delay autoencoders

Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy *

Sparse regression for plasma physics

Data-driven discovery and extrapolation of parameterized pattern-forming dynamics

Multiscale model reduction for incompressible flows

Author Correction: Large area optimization of meta-lens via data-free machine learning

An empirical mean-field model of symmetry-breaking in a turbulent wake

Model predictive control for robust quantum state preparation

Emerging Trends in Machine Learning for Computational Fluid Dynamics

Hierarchical deep learning of multiscale differential equation time-steppers

Enhancing computational fluid dynamics with machine learning

Collaborators (8)

Georgios Rigas

Alan Kaptanoglu

Beverley McKeon

Arka Majumdar

C. Hansen

M. Landreman

Wolfgang Schröder

Jin-Moo Lee

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