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.

翻译：我们展示了一种自动编码的储量流量(AE-RC)方法来学习2D动荡流的动态。 AE-RC方法包括一个自动编码器,它发现了流动状态的高效多重代表,以及一个回声状态网络,它能了解流量的演变时间。 AE-RC既能学习流流流的时间精确动态,又能预测其第一级统计时刻。 AE-RC方法为通过机器学习对动荡进行时空预测开辟了新的可能性。