The growing interest in active nematics and the emerging evidence of the relevance of topological defects in biology asks for reliable data analysis tools to identify, classify and track such defects in simulation and microscopy data. We here provide such tools and demonstrate on two examples, on an active turbulent state in an active nematodynamic model and on emerging nematic order in a multi-phase field model, the possibility to compare statistical data on defect velocities with experimental results. The considered tools, which are physics based and data driven, are compared with each other.

中文翻译：

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有源向列中的缺陷——识别和跟踪算法

对活性向列学的兴趣日益浓厚以及生物学中拓扑缺陷相关性的新证据要求可靠的数据分析工具来识别、分类和跟踪模拟和显微镜数据中的此类缺陷。我们在这里提供了这样的工具，并在两个例子中演示了一个活跃的线动力模型中的活跃湍流状态和一个多相场模型中出现的向列顺序，将缺陷速度的统计数据与实验结果进行比较的可能性。所考虑的基于物理和数据驱动的工具相互比较。