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2021 | OriginalPaper | Chapter

Auto-Encoded Reservoir Computing for Turbulence Learning

Authors : Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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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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previous chapter A Machine Learning Method for Parameter Estimation and Sensitivity Analysis
next chapter Low-Dimensional Decompositions for Nonlinear Finite Impulse Response Modeling
Literature
1.
Brunton, S.L., Noack, B.R., Koumoutsakos, P.: Machine learning for fluid mechanics. Ann. Rev. Fluid Mech. 52(1), 477–508 (2020)CrossRef
2.
Doan, N.A.K., Polifke, W., Magri, L.: Physics-informed echo state networks. J. Comput. Sci. 47, 101237 (2020)
3.
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)MATH
4.
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–15 (2015)
5.
Kolmogorov, A.N.: Dissipation of energy in locally isotropic turbulence. Doklady Akademiia Nauk SSSR 32, 19–21 (1941)MathSciNetMATH
6.
Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)CrossRef
7.
Murata, T., Fukami, K., Fukagata, K.: Nonlinear mode decomposition with convolutional neural networks for fluid dynamics. J. Fluid Mech. 882, A13 (2019)MathSciNetCrossRef
8.
Platt, N., Sirovich, L., Fitzmaurice, N.: An investigation of chaotic Kolmogorov flows. Phys. Fluids A 3(4), 681–696 (1991)MathSciNetCrossRef
9.
Rowley, C.W., Dawson, S.T.: Model reduction for flow analysis and control. Ann. Rev. Fluid Mech. 49, 387–417 (2017)MathSciNetCrossRef
10.
Srinivasan, P.A., Guastoni, L., Azizpour, H., Schlatter, P., Vinuesa, R.: Predictions of turbulent shear flows using deep neural networks. Phys. Rev. Fluids 4, 054603 (2019)
11.
Wan, Z.Y., Vlachas, P., Koumoutsakos, P., Sapsis, T.P.: Data-assisted reduced-order modeling of extreme events in complex dynamical systems. PLoS ONE 13(5), 1–22 (2018)
12.
Yao, J., Hussain, F.: A physical model of turbulence cascade via vortex reconnection sequence and avalanche. J. Fluid Mech. 883, A51 (2020)MathSciNetCrossRef
Metadata
Title
Auto-Encoded Reservoir Computing for Turbulence Learning
Authors
Nguyen Anh Khoa Doan
Wolfgang Polifke
Luca Magri
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77976-4

Electronic ISBN: 978-3-030-77977-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77977-1_27

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