Steven L. Brunton
Steven L. Brunton is an American mechanical engineer and applied mathematician. He is the Boeing Professor of AI & Data-Driven Engineering at the University of Washington, where his research focuses on applying machine learning to dynamical systems, fluid mechanics, and control theory.[1] He serves as Director of both the AI for Engineering Education Institute (AIEEI) and the AI Center for Dynamics and Control (ACDC), and is Associate Director for the NSF AI Institute in Dynamic Systems.[2]
Education and career
Brunton earned a Bachelor of Science degree in mathematics, with a minor in control and dynamical systems, from the California Institute of Technology in 2006.[3] He completed his Ph.D. in mechanical and aerospace engineering at Princeton University in 2012.[4] Following a postdoctoral position in applied mathematics at the University of Washington, he joined the faculty there in 2014, where he is now a full professor.[1]
Research
Brunton’s research combines methods from applied mathematics, machine learning, and physics-based modeling to analyze and control complex systems. His work spans model discovery, reduced-order modeling, sparse sensing, and control, with applications in fluid dynamics, aerospace engineering, energy systems, and neuroscience.[5]
Brunton is noted for developing the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm[6] and for his contributions to data-driven modeling in engineering and the physical sciences, specifically in fluid dynamics.[5]
Awards and honors
- Fellow of the American Physical Society (2024)[7]
- Moore Distinguished Scholar, California Institute of Technology (2021–2022)[8]
Books
- Brunton, S. L., & Kutz, J. N. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019. ISBN 978-1108422093.
- Kutz, J. N., Brunton, S. L., Brunton, B. W., & Proctor, J. L. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems. Society for Industrial and Applied Mathematics (SIAM), 2016. ISBN 978-1-61197-449-2.
- Duriez, T., Brunton, S. L., & Noack, B. R. Machine Learning Control: Taming Nonlinear Dynamics and Turbulence. Springer, 2017. DOI: 10.1007/978-3-319-40624-4.
- Mendez, M. A., Ianiro, A., Noack, B. R., & Brunton, S. L. (Eds.). Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning. Cambridge University Press, 2023. DOI: 10.1017/9781108896214.
References
- ^ a b "Steve Brunton".
- ^ "ME Professor Steve Brunton elected American Physical Society Fellow". 3 October 2024.
- ^ https://campuspubs.library.caltech.edu/2144/1/Commencement_2006.pdf
- ^ "Verifying connection".
- ^ a b S. L. Brunton, B. R. Noack, and P. Koumoutsakos. "Machine Learning for Fluid Mechanics." Annual Review of Fluid Mechanics, 52:477–508, 2020. doi:10.1146/annurev-fluid-010719-060214
- ^ S. L. Brunton, J. L. Proctor, and J. N. Kutz. "Discovering governing equations from data: Sparse identification of nonlinear dynamical systems." Proceedings of the National Academy of Sciences, 113(15):3932–3937, 2016. doi:10.1073/pnas.1517384113
- ^ "APS Fellows Archive".
- ^ "Moore Scholars".