Publications

Dimensionally consistent learning with Buckingham Pi	Bakarji, J., Callaham, J., Brunton, S.L. et al. Dimensionally consistent learning with Buckingham Pi. Nat Comput Sci 2, 834–844 (2022).
A concise guide to modelling the physics of embodied intelligence in soft robotics	Mengaldo, G., Renda, F., Brunton, S.L. et al. A concise guide to modelling the physics of embodied intelligence in soft robotics. Nat Rev Phys 4, 595–610 (2022).
Enhancing computational fluid dynamics with machine learning	Vinuesa, R., Brunton, S.L. Enhancing computational fluid dynamics with machine learning. Nat Comput Sci 2, 358–366 (2022).
An empirical mean-field model of symmetry-breaking in a turbulent wake	Callaham JL, Rigas G, Loiseau JC, Brunton SL. An empirical mean-field model of symmetry-breaking in a turbulent wake. Sci Adv. 2022 May 13;8(19):eabm4786. doi: 10.1126/sciadv.abm4786. Epub 2022 May 11. PMID: 35544559; PMCID: PMC9094670.
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control	Fasel U, Kutz JN, Brunton BW, Brunton SL. 2022 Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.Proc. R. Soc. A 478: 20210904.
Modern Koopman Theory for Dynamical Systems	Brunton, Steven & Budišić, Marko & Kaiser, Eurika & Kutz, J.. (2021). Modern Koopman Theory for Dynamical Systems.
Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization	Baddoo PJ, Herrmann B, McKeon BJ, Brunton SL. 2022 Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization.Proc. R. Soc. A 478: 20210830.
Optimal Sensor and Actuator Selection Using Balanced Model Reduction	K. Manohar, J. N. Kutz and S. L. Brunton, "Optimal Sensor and Actuator Selection Using Balanced Model Reduction," in IEEE Transactions on Automatic Control, vol. 67, no. 4, pp. 2108-2115, April 2022, doi: 10.1109/TAC.2021.3082502.
On the role of nonlinear correlations in reduced-order modelling	Jared L. Callaham, Steven L. Brunton, Jean-Christophe Loiseau. On the role of nonlinear correlations in reduced-order modelling. Journal of Fluid Mechanics, 2022, 938, pp.A1. ff10.1017/jfm.2021.994ff. ffhal-03909105f
PySINDy: A comprehensive Python package for robust sparse system identification	Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Andy J. Goldschmidt, Jared Callaham, Charles B. Delahunt, Zachary G. Nicolaou, Kathleen Champion, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton PySINDy: A comprehensive Python package for robust sparse system identification. Journal of Open Source Software, 7(69), 3994,
Applying machine learning to study fluid mechanics	Brunton, S.L. Applying machine learning to study fluid mechanics. Acta Mech. Sin. 37, 1718–1726 (2021).
Promoting global stability in data-driven models of quadratic nonlinear dynamics	A. A. Kaptanoglu, J. L. Callaham, C. J. Hansen, A. Aravkin, and S. L. Brunton Promoting global stability in data-driven models of quadratic nonlinear dynamics. Physical Review Fluids, 6:094401, 2021. (Editors’ Suggestion)
Sparse nonlinear models of chaotic electroconvection	Y. Guan, S. L. Brunton, and I. Novosselov Sparse nonlinear models of chaotic electroconvection. Royal Society Open Science, 8(8):202367, 2021.
Data-driven aerospace engineering: Reframing the industry with machine learning.	S. L. Brunton, J. N. Kutz, K. Manohar, A. Y. Aravkin, K. Morgansen, J. Klemisch, N. Goebel, J. Buttrick, J. Poskin, A. Blom-Schieber, T. Hogan, and D. McDonald Data-driven aerospace engineering: Reframing the industry with machine learning. AIAA Journal, 59(8):2820–2847, 2021.
Nonlinear stochastic modeling with Langevin regression.	J. L. Callaham, J.-Ch. Loiseau, G. Rigas, and S. L. Brunton Nonlinear stochastic modeling with Langevin regression. Proceedings of the Royal Society A, 477(2250), 2021.
Data-driven discovery of Koopman eigenfunctions for control	E. Kaiser, J. N. Kutz, and S. L. Brunton Data-driven discovery of Koopman eigenfunctions for control. Machine Learning Science and Technology, 2:035023, 2021.
Data-driven resolvent analysis	B. Herrmann, P. J. Baddoo, R. Semaan, S. L. Brunton, and B. J. McKeon Data-driven resolvent analysis. Journal of Fluid Mechanics, 918(A10), 2021.
Learning dominant physical processes with data-driven balance models	J. L. Callaham, J. N. Kutz, B. W. Brunton and S. L. Brunton Learning dominant physical processes with data-driven balance models. Nature Communications, 12(1):1–10, 2021.
Modeling synchronization in forced turbulent oscillator flows	B. Herrmann, P. Oswald, R. Semaan and S. L. Brunton Modeling synchronization in forced turbulent oscillator flows. Communications Physics, 3(195), 2020.
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)
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
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	K. Champion, B. Lusch, J. N. Kutz, and S. L. Brunton Proceedings of the National Academy of Sciences, 116(45):22445–22451, 2019
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