论文标题
基于动力学的机器学习COUETTE流中的过渡
Dynamics-based machine learning of transitions in Couette flow
论文作者
论文摘要
我们在研究最多的规范剪切流中的确切相干状态（ECS）之间的过渡状态（ECS）中得出了低维的数据驱动模型，即平面couette流。这些一维非线性模型代表了吸引光谱亚货号（SSM）的前阶减少动力学，我们使用最近开发的SSMLEARN算法从少量模拟过渡中构造。我们发现能量输入和输出速率为最重要的SSM提供了有效的参数化。通过将动力学限制为这些SSM，我们获得了还原阶模型，这些模型也可靠地预测附近的训练中未使用的外SSM过渡。
We derive low-dimensional, data-driven models for transitions among exact coherent states (ECSs) in one of the most studied canonical shear flows, the plane Couette flow. These one- or two-dimensional nonlinear models represent the leading-order reduced dynamics on attracting spectral submanifolds (SSMs), which we construct using the recently developed SSMLearn algorithm from a small number of simulated transitions. We find that the energy input and output rates provide efficient parametrizations for the most important SSMs. By restricting the dynamics to these SSMs, we obtain reduced-order models that also reliably predict nearby, off-SSM transitions that were not used in their training.
