论文标题

数据驱动的活性列式流体动力学的发现

Data-driven discovery of active nematic hydrodynamics

论文作者

Joshi, Chaitanya, Ray, Sattvic, Lemma, Linnea, Varghese, Minu, Sharp, Graham, Dogic, Zvonimir, Baskaran, Aparna, Hagan, Michael F.

论文摘要

通常使用现象学连续性理论对二维主动夜神经进行建模,该理论通过偏微分方程(PDES)来描述列表主管和流体速度的动力学。尽管这些模型对实验提供了统计准确的描述,但PDE中相关项的识别及其参数通常是间接的。在这里,我们适应了一种最近开发的方法,可以通过稀疏的粗粒磁场稀疏地拟合到通用的低阶PDES,自动从时空主管和速度数据中直接从时空主管和速度数据中自动识别最佳连续模型。我们对计算模型进行了广泛的测试方法,然后将其应用于基于微管的活性列神经实验的数据。因此,我们确定了基于微管的活性列神经的最佳模型以及相关的现象学参数。我们发现,定向领域的动力学在很大程度上受其与基础流的耦合所支配的,而自由能梯度起着可以忽略的作用。此外,通过将流程方程拟合到实验数据中,我们估计一个量化列表的“活性”的关键参数。

Two-dimensional active nematics are often modeled using phenomenological continuum theories that describe the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistically accurate description of the experiments, the identification of the relevant terms in the PDEs and their parameters is usually indirect. Here, we adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from the spatio-temporal director and velocity data, via sparse fitting of the coarse-grained fields onto generic low order PDEs. We test the method extensively on computational models, and then apply it to data from experiments on microtubule-based active nematics. Thereby, we identify the optimal models for microtubule-based active nematics, along with the relevant phenomenological parameters. We find that the dynamics of the orientation field are largely governed by its coupling to the underlying flow, with free-energy gradients playing a negligible role. Furthermore, by fitting the flow equation to experimental data, we estimate a key parameter quantifying the `activity' of the nematic.

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