Learning a compact representation of the nematic director of active nematics

Machine learning has become an important tool for obtaining insights from large experimental datasets on active materials. Because they are intrinsically nonequilibrium and characterized by a broad spectrum of length and time scales, it has been challenging to develop accurate models for the dynamics of active materials using standard statistical physics approaches. In data-driven approaches, neural networks directly learn spatiotemporal correlations in the data to predict future dynamics. In this work, we develop a neural network to learn a compact (reduced-dimension) representation of the Q-tensor field that describes the nematic director of experimental active nematics. Prediction of the active nematics dynamics is then performed in the learned low dimensional space. The compact representation may help to reduce the computation complexity in important downstream tasks such as forecasting and controlling of active nematics.