Dynamical maximum entropy approach to flocking
Dynamical maximum entropy approach to flocking
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DOI：
10.1103/physreve.89.042707
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发表时间：
2014-04-16
影响因子：
2.4
通讯作者：
Walczak, Aleksandra M.
中科院分区：
文献类型：
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作者：
Cavagna, Andrea;Giardina, Irene;Walczak, Aleksandra M.
通过考虑与飞行方向的时间和空间相关性一致的最大熵模型分布，我们推导出一种新方法，从数据中推断集体移动动物群的不平衡排列动态。当鸟类邻域快速发展时，这种动态推理可以正确学习模型的参数，而仅依赖空间相关性的静态推理会失败。当邻居变化缓慢并且满足详细平衡时，我们恢复静态过程。我们在模拟数据上证明了该方法的有效性。该方法适用于其他活性物质系统。
We derive a new method to infer from data the out-of-equilibrium alignment dynamics of collectively moving animal groups, by considering the maximum entropy model distribution consistent with temporal and spatial correlations of flight direction. When bird neighborhoods evolve rapidly, this dynamical inference correctly learns the parameters of the model, while a static one relying only on the spatial correlations fails. When neighbors change slowly and the detailed balance is satisfied, we recover the static procedure. We demonstrate the validity of the method on simulated data. The approach is applicable to other systems of active matter.