An unsupervised neural network learns reproducible and interpretable representations of active matter systems

Active matter systems are high-dimensional. Dimensionality reduction is key to efficient characterization of systems' dynamics, but existing approaches for dimensionality reduction, such as principal-component analysis and singular-value decomposition, can generate representations that are difficult to interpret. We previously presented an unsupervised neural network approach for representing high-dimensional systems. Building on this work, here we present a variational Bayesian method for dimensionality reduction in dynamics data. We show that our unsupervised algorithm learns reproducible and interpretable representations of the system and observe putative currents in the representation phase space. Our data-driven approach facilitates experimental characterization of high-dimensional dynamics, and may also enable irreversibility quantification in general active matter systems.