Auto-Encoded Reservoir Computing for Turbulence Learning

Nguyen Anh Khoa Doan*, Wolfgang Polifke, Luca Magri

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
18 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2021 - 21st International Conference, Proceedings
EditorsMaciej Paszynski, Dieter Kranzlmüller, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. Sloot, Peter M. Sloot, Peter M. Sloot
Number of pages8
ISBN (Print)9783030779764
Publication statusPublished - 2021
Event21st International Conference on Computational Science, ICCS 2021 - Virtual, Online
Duration: 16 Jun 202118 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12746 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Computational Science, ICCS 2021
CityVirtual, Online


  • Autoencoder
  • Echo state network
  • Turbulence


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