Machine learning active-nematic hydrodynamics

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to determine from microscopics. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields encoding the distribution of the energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatio-temporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. By analyzing microtubule-kinesin and actin-myosin experiments as computer vision problems, our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as ATP or motor concentration. They can also forecast the evolution of these chaotic many-body systems solely from image-sequences of their past using a combination of autoencoders and recurrent networks with residual architecture. Our work paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems even when no knowledge of the underlying dynamics exists.