Machine Learning, Dynamical Systems and Control



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 data. We will apply this across a multiple of applications whose objectives are also diverse in nature.


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