Discovering mesoscopic descriptions of collective movement with neural stochastic modelling
This work addresses the challenge of modeling mesoscale collective movement for physicists and engineers, though it appears incremental as it builds on existing neural and stochastic methods.
The researchers tackled the problem of characterizing stochastic collective motion at mesoscales by developing a neural-network-based stochastic differential equation approach, which they applied to synthetic and real-world datasets to identify deterministic and stochastic dynamics, enabling novel inferences about order in these systems.
Collective motion is an ubiquitous phenomenon in nature, inspiring engineers, physicists and mathematicians to develop mathematical models and bio-inspired designs. Collective motion at small to medium group sizes ($\sim$10-1000 individuals, also called the `mesoscale'), can show nontrivial features due to stochasticity. Therefore, characterizing both the deterministic and stochastic aspects of the dynamics is crucial in the study of mesoscale collective phenomena. Here, we use a physics-inspired, neural-network based approach to characterize the stochastic group dynamics of interacting individuals, through a stochastic differential equation (SDE) that governs the collective dynamics of the group. We apply this technique on both synthetic and real-world datasets, and identify the deterministic and stochastic aspects of the dynamics using drift and diffusion fields, enabling us to make novel inferences about the nature of order in these systems.