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Auto-Encoded Reservoir Computing for Turbulence Learning
December 20, 2020 Β· Declared Dead Β· π International Conference on Conceptual Structures
"No code URL or promise found in abstract"
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Authors
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
arXiv ID
2012.10968
Category
physics.flu-dyn
Cross-listed
cs.LG
Citations
10
Venue
International Conference on Conceptual Structures
Last Checked
3 months ago
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.
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