Steve Brunton

About

Steve Brunton is a Professor in Mechanical Engineering at the University of Washington, where he holds the Boeing Professor in AI & Data-Driven Engineering position. He is also a Data Science Fellow at the eScience Institute. His research areas include data science & artificial intelligence, fluids and thermal sciences, and advanced manufacturing. Brunton's work focuses on applying data-driven methods and artificial intelligence to engineering problems, particularly in the context of energy, manufacturing, and fluid dynamics. His contributions involve integrating data science techniques with traditional engineering disciplines to advance understanding and innovation in these fields.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Mechanics
• Engineering
• Data Mining
• Theoretical computer science
• Algorithm
• Aerospace engineering

Selected publications

• Data-Driven Science and Engineering

  2022 · 609 citations

  1st authorCorresponding

  Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with mode…

  DOI

• Enhancing computational fluid dynamics with machine learning

  Nature Computational Science · 2022 · 505 citations

  Senior authorCorresponding
  DOI

• Modern Koopman Theory for Dynamical Systems

  SIAM Review · 2022 · 486 citations

  1st authorCorresponding

  The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science. First-principles derivations and asymptotic reductions are giving way to data-driven approaches that formulate models in operator-theoretic or probabilistic frameworks. Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on…

  DOI

• Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

  Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2022 · 268 citations

  Senior authorCorresponding

  Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identification of the nonlinear dynamics (SINDy) algorithm. First, an ensemble of SINDy models is identified from subsets of limited and noisy data. The aggregate model statistics are then used to produce inclus…

  DOI

• Shallow neural networks for fluid flow reconstruction with limited sensors

  Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2020 · 248 citations

  In many applications, it is important to reconstruct a fluid flow field, or some other high-dimensional state, from limited measurements and limited data. In this work, we propose a shallow neural network-based learning methodology for such fluid flow reconstruction. Our approach learns an end-to-end mapping between the sensor measurements and the high-dimensional fluid flow field, without any heavy preprocessing on the raw data. No prior knowledge is assumed to be available, and the estimation…

  DOI

Recent grants

• AI Institute in Dynamic Systems

  NSF · $19.8M · 2021–2026

Frequent coauthors

• J. Nathan Kutz

  289 shared

• Joshua L. Proctor

  Seattle University

  81 shared

• Bernd R. Noack

  79 shared

• Eurika Kaiser

  60 shared

• Bingni W. Brunton

  University of Washington

  57 shared

• J. Nathan Kutz

  39 shared

• Krithika Manohar

  33 shared

• Benjamin Strom

  University of Washington

  33 shared

Education

• PhD, Mechanical and Aerospace Engineering

  Princeton University

  2012

• BS, Mathematics

  California Institute of Technology

  2006

Awards & honors

• Data Driven Science and Engineering: Machine Learning, Dynam…
• Brunton, Noack, Koumoutsakos. Machine Learning for Fluid Mec…
• Brunton, Proctor, Kutz. Discovering governing equations from…
• Kutz, Brunton, Brunton, Proctor. Dynamic Mode Decomposition:…
• Brunton & Noack. Closed-loop turbulence control: Progress an…

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