Machine Learning, Dynamical Systems and Control

APPLICATIONS: ML FOR DIVERSE, GRAND-CHALLENGE DYNAMICAL SYSTEMS

 

From our fundamental AI advancements in theory and computation, we will port our methods to leading grand challenge problems where model complexity, unknown physics, multiscale and multiphysics phenomena dominate. We will then pursue the following areas. Critical for the reduction of methods to practice is a set of application problems where a common task framework can be used to evaluate methods. This has been exceptionally successful in the computer vision and speech recognition communities where ML/AI has had transformative impact. Our goal is to provide a comprehensive challenge data set framework for evaluating data-driven methods and their performance across a wide range of tasks for dynamical systems from observation datay. The institute will host the common task framework with the following list of realistic goals that may be of interest in various application areas.

 

FACULTY THRUST LEAD

 

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Steven L. Brunton, Associate Director
James Morrison Professor

Website: [ VIEW ]

Department of Mechanical Engineering, Applied Mathematics & eScience Institute

 

Research: Data-driven modeling, control theory, dynamical systems & machine learning, fluid dynamics and turbulence