Publications

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