Machine Learning, Dynamical Systems and Control

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Our mission is to develop the next generation of advanced machine learning tools for controlling complex physical systems by discovering physically interpretable and physics-constrained data-driven models through optimal sensor selection and placement. Our work is anchored by a common task framework that evaluates the performance of machine learning algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. We will push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross-disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data. The common task framework will further support sustainable and open-source challenge datasets, which will be foundational for developing interpretable, ethical, and inclusive tools to solve problems fundamental to human safety, society, and the environment.



Founded Oct. 1, 2021, as part of the National Science Foundation's effort to advance machine learning and AI across the sciences, the Institute is committed to integrating machine learning and artificial intelligence methods for a broad range of scientific and engineering applications. The $20 million investment integrates institutions from the Pacific Northwest (Washington, Montana State, Nevada, Hawaii, Portland State, Boise State and Alaska) with Harvard and Columbia Universities. Our aim is to not only develop fundamental ML/AI methods and algorithms, but also to broadly share these methods with the science and engineering communities by providing open-source code, data, and lectures on the broad and diverse range of topics we consider as an Institute. Thus, we have three key efforts: fundamentals, applications, and education.




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SPOTLIGHT: Upcoming Workshop
The annual CTF Workshop will be held in Honolulu, HI, Feb. 20-21, 2024. We will also offer some sessions online! See Workshops for details & registration!


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November 2, 2023 — Institute postdoctoral scholar, Samuel Otto, University of Washington, selected to present research work at the Division of Engineering and Applied Sciences Trailblazers Symposium at Caltech.


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Partner News
October 23, 2023 — Montana State University has launched The Technology and Artificial Intelligence Learning Seminar (TAILS), a weekly seminar on signals, dynamics, statistics, and machine learning as a part of the NSF AI Institute in Dynamic Systems and the Rocky Mountain Data Science student organization. Check out past projects and learn more about getting involved.


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October 10, 2023 — Institute postdoctoral scholar, Anastasia Bizyaeva, University of Washington, interviewed on experience in a five-week focus period in the field of Network Dynamics and Control organized by the Excellence Center at Linköping – Lund in Information Technology (ELLIIT). Read more from Linköping University news.


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October 9, 2023 — Institute sponsors Autumn School on Scientific Machine Learning and Dynamical Systems with Director Nathan Kutz as co-organizer. This event introduces PhD students, postdocs and early career researchers to the emerging field of Scientific Machine Learning (SciML) in connection with physics applications.




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University of Washington
101 Wilson Annex
Box 352137
Seattle, WA 98195


The AI Institute in Dynamic Systems is one of the National Artificial Intelligence Research Institutes funded by the National Science Foundation (NSF), Award Number 2112085.
Information on the AI Institutes is available at

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