Geometric Principles for Machine Learning Physical Systems

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• Dr Zack X Conti, Alan Turing Institute
• Friday 13 June 2025, 16:00-17:00
• JDB Seminar Room, CUED.

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From classical mechanics, we know that mathematical descriptions of dynamical systems are deeply rooted in topological spaces defined by non-Euclidean geometry. In this talk we will investigate how these structure-rich, geometric representations could be key to improving generalization and parsimony when using machine learning to model physical systems from data.

This talk is part of the Engineering - Dynamics and Vibration Tea Time Talks series.

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