AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data‐driven control

The mission of the AI Institute in Dynamic Systems is to develop the next generation of advanced machine learning (ML) and AI tools for controlling complex physical systems by discovering physically interpretable and physics‐constrained data‐driven models through optimal sensor selection and placeme...

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Bibliographic Details
Published in:The AI magazine 2024-03, Vol.45 (1), p.48-53
Main Authors: Kutz, J. Nathan, Brunton, Steven L., Manohar, Krithika, Lipson, Hod, Li, Na
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Constraints
Control systems
Data collection
Datasets
Decision making
Design engineering
Dynamical systems
Ethics
Interdisciplinary aspects
Machine learning
Optimization
Physics
Science
Sensors
Task complexity
Time series
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Summary:The mission of the AI Institute in Dynamic Systems is to develop the next generation of advanced machine learning (ML) and AI tools for controlling complex physical systems by discovering physically interpretable and physics‐constrained data‐driven models through optimal sensor selection and placement. The research effort is anchored by a common task framework (CTF) that evaluates the performance of ML algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. The aim is to 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 CTF further supports sustainable and open‐source challenge datasets, which are foundational for developing interpretable, ethical, and inclusive tools to solve problems fundamental to human safety, society, and the environment.
ISSN:0738-4602
2371-9621
DOI:10.1002/aaai.12159