Publications

Books

Add some more info about this item...

S. L. Brunton and J. N. Kutz

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

Add some info about this item

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

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

Add some info about this item

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

Please reload

Journal Papers

SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics

https://royalsocietypublishing.org/doi/full/10.1098/rspa.2020.0279

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

Proceedings of the Royal Society A, 476(2242), 2020

Phase-consistent dynamic mode decomposition from multiple overlapping spatial domains

https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.5.074702

A. G. Nair, B. Strom, B. W. Brunton and S. L. Brunton

Physical Review Fluids, 5:074702, 2020

Sensor Selection with Cost Constraints for Dynamically Relevant Bases

https://ieeexplore.ieee.org/document/9099259

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

IEEE Sensors, 20(19):11674--11687, 2020

A unified sparse optimization framework to learn parsimonious physics-informed models from data

https://ieeexplore.ieee.org/document/9194760

K. Champion, P. Zheng, A. Y. Aravkin,  S. L. Brunton, and J. N. Kutz

IEEE Access, 8:169259-169271, 2020. 

Data-driven nonlinear aeroelastic models of morphing wings for control

https://royalsocietypublishing.org/doi/full/10.1098/rspa.2020.0079

N. Fonzi, S. L. Brunton, and U. Fasel

Proceedings of the Royal Society A, 476(2239), 2020

Special issue on machine learning and data-driven methods in fluid dynamics

https://link.springer.com/article/10.1007/s00162-020-00542-y

S. L. Brunton, Maziar S. Hemati, and K. Taira

Theoretical and Computational Fluid Dynamics, 34(4):333--337, 2020

Dimensionality reduction and reduced order modeling for traveling wave physics

https://link.springer.com/article/10.1007/s00162-020-00529-9

 A. Mendible, S. L. Brunton, A. Aravkin, W. Lowrie, and J. N. Kutz

Theoretical and Computational Fluid Dynamics, 34(4):385--400, 2020

Deep Model Predictive Control with Online Learning for Complex Physical Systems

https://link.springer.com/article/10.1007/s00162-020-00520-4

 K. Bieker, S. Peitz, S. L. Brunton, J. N. Kutz, and M. Dellnitz

Theoretical and Computational Fluid Dynamics, 34(4):557--591, 2020

Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data

https://royalsocietypublishing.org/doi/10.1098/rspa.2020.0097

N. B. Erichson, L. Mathelin, Y. Zhewei, S. L. Brunton, M. Mahoney, and J. N. Kutz

Proceedings of the Royal Society A, 476(2238), 2020

Time-Delay Observables for Koopman: Theory and Applications

https://epubs.siam.org/doi/abs/10.1137/18M1216572

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

SIAM Journal on Dynamical Systems, 19(2):886--917, 2020

Randomized CP Tensor Decomposition

https://iopscience.iop.org/article/10.1088/2632-2153/ab8240/meta

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

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

Robust Principal Component Analysis for Particle Image Velocimetry

https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.5.054401

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

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

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

https://ieeexplore.ieee.org/document/9044302

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

Sparse principal component analysis via variable projection

https://epubs.siam.org/doi/abs/10.1137/M1124176

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

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

https://joss.theoj.org/papers/10.21105/joss.02104

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

Discovery of physics from data: Universal laws and discrepancy models

https://www.frontiersin.org/articles/10.3389/frai.2020.00025/full

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

Modal Analysis of Fluid Flows: Applications and Outlook

https://arc.aiaa.org/doi/full/10.2514/1.J058462

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

Characterizing magnetic plasmas with dynamic mode decomposition

https://aip.scitation.org/doi/abs/10.1063/1.5138932

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

Physics of Plasmas, 27:032108, 2020

Dynamic mode decomposition for compressive system identification

https://arc.aiaa.org/doi/full/10.2514/1.J057870

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

AIAA Journal, 58(2):561–574, 2020

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

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

Randomized methods to characterize large-scale vortical flow networks

https://doi.org/10.1371/journal.pone.0225265

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

PLoS ONE, 14(11):e0225265, 2019

Data-driven discovery of coordinates and governing equations

https://www.pnas.org/content/116/45/22445.abstract

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

Robust flow reconstruction from limited measurements via sparse representation

https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.4.103907

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

Randomized dynamic mode decomposition

https://epubs.siam.org/doi/abs/10.1137/18M1215013

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

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

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

Journal of Computational Physics , 398:108860 , 2019

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

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

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

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

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

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

Journal of Physics: Materials , 2:044002 , 2019

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

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

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

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

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

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

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

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

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

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

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

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

Add some more info about this item...

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

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

Add some more info about this item...

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

Fuel, 236:583--588, 2019

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

Physical Review E, 98:062312, 2018

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

Nature Communications, 9(1):4950, 2018

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

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

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

Complexity, 2018(6010634), 2018

Add some more info about this item...

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

PLoS ONE, 13(11):e0207215, 2018

Add some more info about this item...

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

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

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

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

Chaos, 28(6):063116, 2018

Add some more info about this item...

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

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

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

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

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

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

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

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

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

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