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Found 4 papers, 2 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.
Demographic noise can promote abrupt transitions in ecological systems
In this paper, we show that demographic noise may, in fact, promote abrupt transitions in systems that would otherwise show continuous transitions.
