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 NSF AI Institute in Dynamic Systems, the AI Center for Dynamics and Control (ACDC), and the AI for Engineering Education Institute (AIEEI).[2]
Education and career
[edit]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
[edit]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 non-linear 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
[edit]- Fellow of the American Physical Society (2024)[7]
- Moore Distinguished Scholar, California Institute of Technology (2021–2022)[8]
- Presidential Early Career Award for Scientists and Engineers (PECASE) (2019)[9]
Books
[edit]- 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
[edit]- 1 2 "Steve Brunton".
- ↑ "ME Professor Steve Brunton elected American Physical Society Fellow". 3 October 2024.
- ↑ "California Institute of Technology Commencement Program 2006" (PDF). Caltech Campus Publications. 2006. Retrieved 2026-02-20.
- ↑ "Steven L. Brunton – Princeton University Archives". Princeton University. Retrieved 2026-02-20.
- 1 2 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".
- ↑ "UW's Steve Brunton receives Presidential Early Career Award for Scientists and Engineers". University of Washington News. July 9, 2019. Retrieved 2026-02-20.
