Machine learning active-nematic hydrodynamics
Machine learning active-nematic hydrodynamics
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DOI：
10.1073/pnas.2016708118
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发表时间：
2021-03-09
影响因子：
11.1
通讯作者：
Vitelli, Vincenzo
中科院分区：
文献类型：
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作者：
Colen, Jonathan;Han, Ming;Vitelli, Vincenzo
流体力学理论有效地描述了多体系统的平衡在一些宏观参数。然而，这些参数很难从微观信息中确定。这种挑战很少比在活性物质中更明显，在活性物质中，流体动力学参数实际上是编码能量注入微观成分分布的场。在这里，我们使用主动向列型来证明神经网络可以映射出多个流体动力学参数的时空变化，并预测这些系统的混沌动力学。我们分析生物丝/分子马达实验微管/驱动蛋白和肌动蛋白/肌球蛋白复合物作为计算机视觉问题。我们的算法可以确定活动和弹性模量如何随空间和时间以及三磷酸腺苷（ATP）或运动浓度而变化。唯一需要的输入是生物丝的方向，而不是耦合的速度场，这在实验中很难获得。我们还可以预测这些混沌多体系统的演变，仅仅从他们过去的图像序列，使用自编码器和递归神经网络与残差架构的组合。在初始条件不完全已知的现实实验设置中，我们的物理启发机器学习算法可以超越确定性模拟。我们的研究为人工智能表征和控制不同物理和生物系统中的耦合混沌场铺平了道路，即使在缺乏基础动力学知识的情况下。
Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.