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MLDADS 2021 - Auto Encoded Reservoir Computing for Turbulence Learning

Presentation by Anh Khoa for the Data Learning working group on ‘Auto-Encoded Reservoir Computing for Turbulence Learning’. This presentation was recorded for MLDADS 2021 - ICCS 2021. Authors included in this work are: Nguyen Anh Khoa Doan, Wolfgang Polifke and Luca Magri. MLDADS 2021: https://www.imperial.ac.uk/events/124... Data Learning Working Group: https://sites.google.com/view/rossell... 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 a Convolutional 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 turbulent flow and predict its first-order statistical moments. The AE-RC approach opens up new possibilities for the spatio-temporal prediction of turbulent flows with machine learning.