Auto-Encoded Reservoir Computing for Turbulence Learning

2020/12/20 by Doan, Nguyen Anh Khoa, Polifke, Wolfgang, Magri, Luca
#FOS: Computer and information sciences #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2012.10968

Abstract

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.

Related

• Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
• A physics-aware machine to predict extreme events in turbulence
• Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network
• Autoregressive Transformers for Data-Driven Spatio-Temporal Learning of Turbulent Flows
• Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models