mlsanityData to 14 Oct 2026

Auto-Encoded Reservoir Computing for Turbulence Learning

arXiv 2012.10968 ↗First posted 20 Dec 2020physics.flu-dyn cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 1; 2026: 1.

7 papers.

  1. 2022Data-driven low-dimensional dynamic model of Kolmogorov flowcited by 9
  2. 2021Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary gridscited by 6
  3. 2024Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Datacited by 3
  4. 2022Statistical prediction of extreme events from small datasetscited by 2
  5. 2023Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flowcited by 2
  6. 2025Online model learning with data-assimilated reservoir computerscited by 2
  7. 2026Spectrally Regularized Latent Flow Matching for Turbulence Generationcited by 0