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Dimensionally consistent learning with Buckingham Pi
A concise guide to modelling the physics of embodied intelligence in soft robotics
Enhancing computational fluid dynamics with machine learning
An empirical mean-field model of symmetry-breaking in a turbulent wake
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
Modern Koopman Theory for Dynamical Systems
Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization
Optimal Sensor and Actuator Selection Using Balanced Model Reduction
On the role of nonlinear correlations in reduced-order modelling
PySINDy: A comprehensive Python package for robust sparse system identification
Applying machine learning to study fluid mechanics
Promoting global stability in data-driven models of quadratic nonlinear dynamics
Sparse nonlinear models of chaotic electroconvection
Data-driven aerospace engineering: Reframing the industry with machine learning.
Nonlinear stochastic modeling with Langevin regression.
Data-driven discovery of Koopman eigenfunctions for control
Data-driven resolvent analysis
Learning dominant physical processes with data-driven balance models
Modeling synchronization in forced turbulent oscillator flows
Robust Principal Component Analysis for Particle Image Velocimetry
Modal Analysis of Fluid Flows: Applications and Outlook
Machine Learning for Fluid Mechanics

S. L. Brunton, B. R. Noack, and P. Koumoutsakos

Annual Review of Fluid Mechanics, 52:477--508, 2020

Data-driven discovery of coordinates and governing equations
Randomized Matrix Decompositions using R

N. B. Erichson, S. Voronin, S. L. Brunton, and J. N. Kutz

Journal of Statistical Software , 89(11):1–48 , 2019

A Unified Framework for Sparse Relaxed Regularized Regression: SR3

P. Zheng, T. Askham, S. L. Brunton, J. N. Kutz, and A. Y. Aravkin

IEEE Access, 7(1):1404--1423, 2019

Deep learning for universal linear embeddings of nonlinear dynamics

B. Lusch, J. N. Kutz, S. L. Brunton

Nature Communications, 9(1):4950, 2018

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

E. Kaiser, J. N. Kutz, and S. L. Brunton

Proceedings of the Royal Society A, 474(2219), 2018

Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data

T. Mohren, T. L. Daniel, S. L. Brunton, and B. W. Brunton

Proceedings of the National Academy of Sciences, 115(42):10564–10569, 2018

Predicting shim gaps in aircraft assembly with machine learning and sparse sensing

K. Manohar, T. Hogan, J. Buttrick, A. G. Banerjee, J. N. Kutz, and S. L. Brunton

Journal of Manufacturing Systems, 48(C):87-95, 2018

Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

K. Manohar, B. W. Brunton, J. N. Kutz, and , S. L. Brunton

IEEE Control Systems Magazine, 38(3):63-86, 2018

Sparse reduced-order modeling: Sensor-based dynamics to full-state estimation

J. C. Loiseau, B. R. Noack, and S. L. Brunton

Journal of Fluid Mechanics, 844:459–490, 2018

Constrained sparse Galerkin regression

J. C. Loiseau and S. L. Brunton

Journal of Fluid Mechanics, 838:42–67, 2018

Modal Analysis of Fluid Flows: An Overview

K. Taira, S. L. Brunton, S. T. M. Dawson, C. W. Rowley, T. Colonius, B. J. McKeon, O. Schmidt, S. Gordeyev, V. Theofilis, and L. S. Ukeiley

AIAA Journal, 55(12):4013–4041, 2017

Intracycle angular velocity control of cross-flow turbines

B. Strom, S. L. Brunton, and B. Polagye

Nature Energy, 2(17103):1–9, 2017

Chaos as an intermittently forced linear system

S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, and J. N. Kutz

Nature Communications, 8(19):1–9, 2017

Data-driven discovery of partial differential equations

S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz

Science Advances, 3:e1602614, 2017

Sparse sensor placement optimization for classification

B. W. Brunton, S. L. Brunton, J. L. Proctor, and J. N. Kutz

SIAM Journal on Applied Mathematics, 76(5):2099–2122, 2016

Discovering governing equations from data: Sparse identification of nonlinear dynamical systems

S. L. Brunton, J. L. Proctor, and J. N. Kutz

Proceedings of the National Academy of Sciences, 113(15):3932-3937, 2016

Network Structure of Two-Dimensional Isotropic Turbulence

K. Taira, A. G. Nair, and S. L. Brunton

Journal of Fluid Mechanics, 795(R2):1–11, 2016

Finite-time Lyapunov exponents for inertial particles in an unsteady fluidsentations of nonlinear dynamical systems for control

S. Madhavan, S. L. Brunton, and J. J. Riley

Physical Review E, 93:033108, 2016

Closed-loop turbulence control: Progress and challenges

S. L. Brunton and B. R. Noack
Applied Mechanics Reviews, 67(5):050801-1–050801-48, 2015

Generalizing dynamic mode decomposition to a larger class of datasets

J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz
Journal of Computational Dynamics, 1(2):391–421, 2014

Long-time uncertainty propagation using generalized polynomial chaos and flow map composition

D. M. Luchtenburg, S. L. Brunton, and C. W. Rowley
Journal of Computational Physics, 274:783–802, 2014

State-space identification of reduced-order unsteady aerodynamic models for feedback control

S. L. Brunton, S. T. M. Dawson, and C. W. Rowley
Journal of Fluids and Structures, 50:253–270, 2014

Reduced-order unsteady aerodynamic models at low Reynolds numbers

S. L. Brunton, C. W. Rowley, and D. R. Williams
Journal of Fluid Mechanics, 724:203–233, 2013

Fast computation of finite-time Lyapunov exponent fields for unsteady flows

S. L. Brunton and C. W. Rowley

Chaos 20, 017503, 2010

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