Publications

Books

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

S. L. Brunton and J. N. Kutz

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Book website (code and videos): http://databookuw.com/

Amazon

Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems

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

Amazon

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

T. Duriez, S. L. Brunton, B. R. Noack

Amazon
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Journal Papers

Randomized CP Tensor Decomposition

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

Machine Learning: Science and Technology, 1(2):025012, 2020

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Robust Principal Component Analysis for Particle Image Velocimetry

I. Scherl, B. Strom, J. K. Shang, O. Williams, B. L. Polagye, and S. L. Brunton

Physical Review Fluids, 5:054401, 2020. (Editors’ Suggestion)

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Learning precisely timed feedforward control of the sensor-denied inverted pendulum

T. L. Mohren, T. L. Daniel, and S. L. Brunton
IEEE Control System Letters, 4(3):731–736, 2020

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Sparse principal component analysis via variable projection

N. B. Erichson, P. Zeng, K. Manohar, S. L. Brunton, J. N. Kutz, and A. Y. Aravkin

SIAM Journal on Applied Mathematics, 80(2):977–1002, 2020

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PySINDy: A Python Package for the Sparse Identification of Dynamics from Data

B. de Silva, K. Champion, M. Quade, J.-Ch. Loiseau, J. N. Kutz, and S. L. Brunton

Journal of Open Source Software, 5(49): 2104, 2020

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Discovery of physics from data: Universal laws and discrepancy models

B. de Silva, D. M. Higdon, S. L. Brunton, and J. N. Kutz
Frontiers in Artificial Intelligence, 3:1–25, 2020

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Modal Analysis of Fluid Flows: Applications and Outlook

K. Taira, M. S. Hemati, S. L. Brunton, Y. Sun, K. Duraisamy, S. Bagheri, S. T. M. Dawson, and C.-A. Yeh

AIAA Journal, 58(3):1–25, 2020

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Characterizing magnetic plasmas with dynamic mode decomposition

A. A. Kaptanoglu, K. D. Morgan, C. J. Hansen, and S. L. Brunton

Physics of Plasmas, 27:032108, 2020

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Dynamic mode decomposition for compressive system identification

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

AIAA Journal, 58(2):561–574, 2020

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Machine Learning for Fluid Mechanics

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

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

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Randomized methods to characterize large-scale vortical flow networks

Z. Bai, N. B. Erichson, M. Gopalakrishnan Meena, K. Taira, and S. L. Brunton

PLoS ONE, 14(11):e0225265, 2019

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Data-driven discovery of coordinates and governing equations

K. Champion, B. Lusch, J. N. Kutz, and S. L. Brunton
Proceedings of the National Academy of Sciences, 116(45):22445–22451, 2019

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Robust flow reconstruction from limited measurements via sparse representation

J. L. Callaham, K. Maeda, and S. L. Brunton
Physical Review Fluids, 4:103907, 2019

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Randomized dynamic mode decomposition

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

SIAM Journal on Applied Dynamical Systems, 18(4):1867–1891, 2019

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Discovering time-varying aeroelastic models of a long-span suspension bridge from field measurements by sparse identification of nonlinear dynamical systems

S. Li, E. Kaiser, S. Laima, H. Li, S. L. Brunton, and J. N. Kutz

Physical Review E, 100(2):022220, 2019

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Smoothing and parameter estimation by soft-adherence to governing equations

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

Journal of Computational Physics , 398:108860 , 2019

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Deep learning of dynamics and signal–noise decomposition with time-stepping constraints

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

Journal of Computational Physics , 396:483–506 , 2019

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RetinaMatch: Efficient Template Matching of Retina Images for Teleopthamology

C. Gong, N. B. Erichson, J. P. Kelly, L. Trutoiu, B. T. Schowengerdt, S. L. Brunton, and E. Seibel

IEEE Transactions on Medical Imaging , 38(8):1993–2004 , 2019

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Cluster-based feedback control of turbulent post-stall separated flows

A. Nair, C.-A. Yeh, E. Kaiser, B. R. Noack. S. L. Brunton, and K. Taira

Journal of Fluid Mechanics , 875:345--375 , 2019

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Methods for data-driven multiscale model discovery for materials

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

Journal of Physics: Materials , 2:044002 , 2019

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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

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Data-driven identification of parametric partial differential equations

S. Rudy, A. Alla, S. L. Brunton, and J. N. Kutz

SIAM Journal on Applied Dynamical Systems, 18(2):643–660, 2019

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Greedy sensor placement with cost constraints

E. Clark, T. Askham, S. L. Brunton, and J. N. Kutz

IEEE Sensors Journal, 19(7):2642–2656, 2019

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Model selection for hybrid dynamical systems via sparse regression

N. M. Mangan, T. Askham, S. L. Brunton, J. N. Kutz, and J. L. Proctor

Proceedings of the Royal Society A, 475(20180534), 2019

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Discovery of nonlinear multiscale systems: Sampling strategies and embeddings

K. P. Champion, S. L. Brunton, and J. N. Kutz

SIAM Journal on Applied Dynamical Systems, 18(1):312–333, 2019

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Optimized sampling for multiscale dynamics

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

SIAM Multiscale Modeling and Simulation, 17(1):117–136, 2019

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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

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Prevention of Lean Flame Blowout Using a Predictive Chemical Reactor Network Control

S. Gupta, P. Malte, S. L. Brunton, I. Novosselov

Fuel, 236:583--588, 2019

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Feedback through graph motifs relates structure and function in complex networks

Y. Hu, S. L. Brunton, N. Cain, S. Mihalas, J. N. Kutz, and E. Shea-Brown

Physical Review E, 98:062312, 2018

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Deep learning for universal linear embeddings of nonlinear dynamics

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

Nature Communications, 9(1):4950, 2018

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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

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Koopman Theory for Partial Differential Equations

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

Complexity, 2018(6010634), 2018

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Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

M. Au-Yeung, P. G. Reinhall, G. Bardy, and S. L. Brunton

PLoS ONE, 13(11):e0207215, 2018

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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

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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

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Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery

M. Quade, M. Abel, J. N. Kutz, and S. L. Brunton

Chaos, 28(6):063116, 2018

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Networked oscillator based modeling and control of unsteady fluid flows

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

Physical Review E., 97(6):063107-1–063107-14, 2018

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Sparse-TDA: Sparse realization of topological data analysis for multi-way classification

W. Guo, K. Manohar, S. L. Brunton, and A. G. Banerjee

IEEE Transactions on Knowledge and Data Engineering, 30(7):1403-1408, 2018

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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

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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

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Generalizing Koopman theory to allow for inputs and control

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

SIAM Journal of Dynamical Systems, 17(1):909-930, 2018

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Online interpolation point refinement for reduced order models using a genetic algorithm

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

SIAM Journal on Scientific Computing, 40(1):B283–B304, 2018

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Deep learning and model predictive control for self-tuning mode-locked lasers

T. Baumeister, S. L. Brunton, and J. N. Kutz

J. Optical Society of America B, 35(3): 617–626, 2018

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Constrained sparse Galerkin regression

J. C. Loiseau and S. L. Brunton

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

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Sparsity enabled cluster reduced-order modeling for control

E. Kaiser, M. Morzynski, G. Daviller, J. N. Kutz, B. Brunton, and S. L. Brunton

Journal of Computational Physics, 352:388–409, 2018

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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

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