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Draft:Steven L. Brunton

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  • Comment: Still tone issues, particularly in the lead. I'm not seeing many (any?) secondary sources. Also consider REFBOMB, particularly in the "Research Interests" section. Anerdw (talk) 02:45, 21 June 2025 (UTC)
  • Comment: As a Fellow of the American Physical Society Brunton meets the requirements for an article given in WP:NPROF. Hwever this is written like a public relations piece, not an encyclopedia article. Leave out all the wonderfulness stuff and PR-speak like "leveraging". Every statement in an article about a living person must have a reliably published source for verification. Remove anything than doesn't. You have much too close paraphrasing of sentences taken from his 2021 Moore scholar award - Wikipedia takes copyright violation very seriously. Rewrite this in your own words. StarryGrandma (talk) 02:13, 8 June 2025 (UTC)
  • Comment: In accordance with Wikipedia's Conflict of interest policy, I disclose that I have a conflict of interest regarding the subject of this article. Lileaas (talk) 01:07, 7 June 2025 (UTC)

Steven L. Brunton is an American academic in the fields of mechanical engineering, applied mathematics, and machine learning. He holds the position of Boeing Professor in AI & Data Driven Engineering in the Department of Mechanical Engineering at the University of Washington (UW). He is a Data Science Fellow at the eScience Institute and serves as Director of both the AI for Engineering Education Institute (AIEEI) and the AI Center for Dynamics and Control (ACDC). He is also Associate Director for the NSF AI Institute in Dynamic Systems. He has adjunct professorships in applied mathematics, computer science, and aeronautics and astronautics at UW.

Brunton has co-authored over 200 scientific publications and several textbooks. He has mentored more than 30 PhD students and over 15 postdocs, and has led or collaborated on numerous funded research projects. He also maintains a YouTube channel, "eigensteve", where he shares educational videos on dynamics and machine learning.[1] [2] [3]

Education and Early Career
[edit]

Brunton completed his Bachelor of Science in Mathematics, with a minor in Control and Dynamical Systems, at the California Institute of Technology (Caltech) in 2006. He then pursued his doctoral studies at Princeton University, earning a Ph.D. in Mechanical and Aerospace Engineering in 2012.[4]

Following his doctorate, he joined the University of Washington as an Acting Assistant Professor in Applied Mathematics (2012–2014) before transitioning to the Mechanical Engineering department.[5]

Research Interests
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Brunton's research combines machine learning with dynamical systems to model and control complex systems, with a particular focus on applications in fluid dynamics. He also works on applied topics including manufacturing, energy systems, biolocomotion, and neuroscience. His work includes the development of sparse optimization methods for discovering nonlinear models, sensor and actuator placement, and control strategies. Brunton has worked closely with Boeing for over a decade, with his algorithms currently in production on several aircraft manufacturing lines.

  • Machine learning for dynamical systems to model and control complex systems: Brunton's research focuses on the intersection of machine learning and dynamical systems, particularly for modeling and controlling complex systems in fluid dynamics.[6] [7] [8] [9]
  • Fluid dynamics: Brunton and collaborators have been active in using machine learning to improve the modeling and control of fluid dynamics systems. These efforts involve dimensionality reduction, reduced-order modeling, sparse sensing, and control.[10] [11] [12] [13] [14]
  • Aerospace engineering and sparse sensing: Working with Boeing, Brunton has developed efficient sensing algorithms and technical roadmaps for how to incorporate digital design and machine learning into modern aerospace engineering workflows.[15] [16] [17]
Publications and Contributions
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Brunton has co-authored several textbooks that bridge the gap between machine learning and engineering disciplines:

  • Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control[18]
  • Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems[19]
  • Data Driven Fluid Mechanics: Combining First Principles and Machine Learning[20]
  • Machine Learning Control: Taming Nonlinear Dynamics and Turbulence[21]

In 2024, Brunton was named a Highly Cited Researcher, which identifies scientists whose work ranks among the top 1% most cited in their field.[22] [23]

Awards and Honors
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Throughout his career, Brunton has been recognized with numerous awards, including:

  • Fellow of the American Physical Society (2024)[24]
  • Moore Distinguished Scholar, Sabbatical at Caltech (2021–2022)[25]
  • Presidential Early Career Award for Scientists and Engineers (PECASE) (2019)[26]
  • Simons Participant, Institute for Pure and Applied Mathematics (IPAM) UCLA (2019)[27]
  • SIAM Computational Science and Engineering Early Career Prize (2019)[28]
  • UW College of Engineering Junior Faculty Award (2018)
  • Air Force Young Investigator Program (YIP) Award (2017)
  • Army Young Investigator Program (YIP) Award (2017)
  • UW College of Engineering Faculty Teaching Award (2017)
  • Data Science Fellow, eScience Institute (2014)[29]
Educational Outreach
[edit]

Beyond his research, Brunton is dedicated to providing accessible education to the public. He operates a popular YouTube channel under the name "eigensteve," where he teaches topics related to dynamics, control, and machine learning, making complex subjects available to a broader audience.[30]

Professional Affiliations
[edit]

Brunton is actively involved in various professional communities and serves as a Data Science Fellow at UW's eScience Institute, contributing to the advancement of interdisciplinary data science research.[31]

References

[edit]
  1. ^ https://www.me.washington.edu/
  2. ^ https://www.eigensteve.com/
  3. ^ "Steve Brunton". YouTube.
  4. ^ https://www.me.washington.edu/
  5. ^ https://www.eigensteve.com/
  6. ^ 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
  7. ^ S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz. Data-driven discovery of partial differential equations. Science Advances, 3:e1602614, 2017. doi:10.1126/sciadv.1602614
  8. ^ J. C. Loiseau and S. L. Brunton. Constrained sparse Galerkin regression. Journal of Fluid Mechanics, 838:42–67, 2018. doi:10.1017/jfm.2017.823
  9. ^ S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, and J. N. Kutz. Chaos as an intermittently forced linear system. Nature Communications, 8, 2017. doi:10.1038/s41467-017-00030-8
  10. ^ 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
  11. ^ 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. Modal Analysis of Fluid Flows: An Overview. AIAA Journal, 55(12):4013–4041, 2017. doi:10.2514/1.J056060
  12. ^ J. L. Callaham, G. Rigas, J.-Ch. Loiseau, and S. L. Brunton. An empirical mean-field model of symmetry-breaking in a turbulent wake. Science Advances, 8:eabm4786, 2022. doi:10.1126/sciadv.abm4786
  13. ^ R. Vinuesa and S. L. Brunton. Enhancing computational fluid dynamics with machine learning. Nature Computational Science, 2:358–366, 2022. doi:10.1038/s43588-022-00264-7
  14. ^ R. Vinuesa, S. L. Brunton, and B. J. McKeon. The Transformative Potential of Machine Learning for Experiments in Fluid Mechanics. Nature Reviews Physics, 5:536–545, 2023. doi:10.1038/s42254-023-00622-y
  15. ^ 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. doi:10.2514/1.J060131
  16. ^ K. Manohar, T. Hogan, J. Buttrick, A. G. Banerjee, J. N. Kutz, and S. L. Brunton. Predicting shim gaps in aircraft assembly with machine learning and sparse sensing. Journal of Manufacturing Systems, 48(Part C):87–95, 2018. doi:10.1016/j.jmsy.2018.01.011
  17. ^ T. Mohren, T. L. Daniel, S. L. Brunton, and B. W. Brunton. Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data. Proceedings of the National Academy of Sciences, 115(42):10564–10569, 2018. doi:10.1073/pnas.1808909115
  18. ^ S. L. Brunton and J. N. Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019. [1]
  19. ^ J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. Proctor. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems. SIAM, 2016. [2]
  20. ^ M. A. Mendez, A. Ianiro, B. R. Noack, and S. L. Brunton (Eds.). Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning. Cambridge University Press, 2023. doi:10.1017/9781108896214
  21. ^ T. Duriez, S. L. Brunton, and B. R. Noack. Machine Learning Control: Taming Nonlinear Dynamics and Turbulence. Springer, 2016. doi:10.1007/978-3-319-40624-4
  22. ^ "Google Scholar".
  23. ^ "Highly Cited Researchers | Clarivate". 13 November 2024.
  24. ^ "APS Fellows Archive".
  25. ^ "Moore Scholars".
  26. ^ "Presidential Early Career Award for Scientists and Engineers".
  27. ^ "Machine Learning for Physics and the Physics of Learning - IPAM". 9 February 2018.
  28. ^ "January Prize Spotlight: Jeff Bezanson, Steven L. Brunton, Jack Dongarra, Stefan Karpinski, and Viral B. Shah | SIAM". 4 January 2019.
  29. ^ https://www.eigensteve.com/
  30. ^ "Steve Brunton". YouTube.
  31. ^ https://www.me.washington.edu/
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