Machine learning for active matter

Frank Cichos, Kristian Gustavsson, Bernhard Mehlig, Giovanni Volpe

Abstract: This Review surveys machine learning techniques that are currently developed for a range of research topics in biological and artificial active matter and also discusses challenges and exciting opportunities. This research direction promises to help disentangle the complexity of active matter and gain fundamental insights for instance in collective behaviour of systems at many length scales from colonies of bacteria to animal flocks.