Publications
Books
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S. L. Brunton and J. N. Kutz
Book website (code and videos): http://databookuw.com/
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J. N. Kutz, S. L. Brunton, B. W. Brunton, J. L. Proctor
Book website (code and videos): http://dmdbook.com/
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T. Duriez, S. L. Brunton, B. R. Noack
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. Li, E. Kaiser, S. Laima, H. Li, S. L. Brunton, and J. N. Kutz
Physical Review E, 100(2):022220, 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
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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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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
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M. Au-Yeung, P. G. Reinhall, G. Bardy, and S. L. Brunton
PLoS ONE, 13(11):e0207215, 2018
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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
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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
