DySIB recovers the two-dimensional phase space of a physical pendulum from experimental video by optimizing a symmetric information bottleneck objective entirely in latent space.
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Data-driven framework using short-time TUR inference and deep neural networks reconstructs high-dimensional dissipative force fields and localizes fluctuating entropy production in space and time.
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Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
DySIB recovers the two-dimensional phase space of a physical pendulum from experimental video by optimizing a symmetric information bottleneck objective entirely in latent space.