论文标题
用于湍流学习的自动编码储层计算
Auto-Encoded Reservoir Computing for Turbulence Learning
论文作者
论文摘要
我们提出了一种自动编码的储层计算（AE-RC）方法，以了解2D湍流的动力学。 AE-RC由一个自动编码器组成，该自动编码器发现了流量状态的有效多种形式表示，以及一个回声状态网络，该网络了解了流动中流的时间演变。 AE-RC能够学习流动的时间准确的动力学，并预测其一阶统计矩。 AE-RC方法为通过机器学习对湍流的时空预测开辟了新的可能性。
We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the dynamics of a 2D turbulent flow. The AE-RC consists of an Autoencoder, which discovers an efficient manifold representation of the flow state, and an Echo State Network, which learns the time evolution of the flow in the manifold. The AE-RC is able to both learn the time-accurate dynamics of the flow and predict its first-order statistical moments. The AE-RC approach opens up new possibilities for the spatio-temporal prediction of turbulence with machine learning.
