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Found 5 papers, 3 papers with code
Data-driven discovery of stochastic dynamical equations of collective motion
We consider k = 1 (called stochastic pairwise interactions), k = 2 (stochastic ternary interactions), and k equalling all available neighbours within the interaction radius (equivalent to Vicsek-like local averaging).
Discovering mesoscopic descriptions of collective movement with neural stochastic modelling
Collective motion is an ubiquitous phenomenon in nature, inspiring engineers, physicists and mathematicians to develop mathematical models and bio-inspired designs.
Discovering stochastic dynamical equations from biological time series data
Stochastic differential equations (SDEs) are an important framework to model dynamics with randomness, as is common in most biological systems.
Disentangling intrinsic motion from neighbourhood effects in heterogeneous collective motion
Since collective effects such as jamming and clustering affect individual motion, an agent's own movement does not have sufficient information to perform the classification well: a simple observer algorithm, based only on individual velocities cannot accurately estimate the level of heterogeneity of the system, and often misclassifies agents.
Mapping distinct timescales of functional interactions among brain networks
First, we show, with simulated fMRI data, that instantaneous and lag-based GC identify distinct timescales and complementary patterns of functional connectivity.
