Analyzing collective motion with machine learning and topology
Analyzing collective motion with machine learning and topology
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DOI：
10.1063/1.5125493
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发表时间：
2019-12-01
期刊：
影响因子：
2.9
通讯作者：
Ziegelmeier, Lori
中科院分区：
文献类型：
--
作者：
Bhaskar, Dhananjay;Manhart, Angelika;Ziegelmeier, Lori
我们使用拓扑数据分析和机器学习来研究生物学中集体运动的一个开创性模型[M。R. D 'Orsogna等人，物理修订信函96，104302（2006）]。该模型描述了通过吸引-排斥社会力进行非线性交互的代理，并产生了群集和铣削等集体行为。为了对大型数值模拟库中的紧急集体运动进行分类，并从模拟数据中恢复模型参数，我们将机器学习技术应用于两种不同类型的输入。首先，我们输入传统上用于集体运动研究的序参量的时间序列。其次，我们输入的措施，总结了多个尺度上的模拟数据随时间变化的持续同源性的拓扑结构的基础上。这种拓扑方法不需要预期模式的先验知识。对于无监督和有监督的机器学习方法，拓扑方法优于基于传统序参数的方法。(C)2019年作者。
We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D'Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters. (C) 2019 Author(s).