Computational and Data Driven Active Nematics

Active nematic liquid crystals are a class of non-equilibrium systems with constituents that generate motion by consuming energy at the molecular level. Active nematics are often studied using phenomenological theories that describe the time evolution of the nematic director and fluid velocity through partial differential equations. In this talk, we use computational and data-driven techniques to model active nematics. We infer key information about the model, such as the active time scale and director dynamics, directly from the experimental data of 2D active nematics. Further, we investigate the steady state behavior of some of these models under various biologically relevant scenarios. Our work provides crucial steps in the development of programmable active matter using model-dependent methods.