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The most robust representations of flow trajectories are Lagrangian coherent structures

Published online by Cambridge University Press:  28 September 2021

Theodore MacMillan Open the ORCID record for Theodore MacMillan [Opens in a new window]  and
David H. Richter Open the ORCID record for David H. Richter [Opens in a new window]
Theodore MacMillan
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46656, USA
David H. Richter*
Affiliation:
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, IN 46656, USA
*
†Email address for correspondence: [email protected]
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Abstract

What is the most robust way to communicate flow trajectories? To answer this question, we employ two neural networks to respectively deconstruct (the encoder) and reconstruct (the decoder) trajectories, where information is passed between the two networks through a low-dimensional latent space in a set-up known as an autoencoder. To ensure that their communications are robust, we add noise to the coded information passed through this latent space. In the low-noise limit the latent space structures are non-spatial in nature, resembling modes of a principle component analysis (PCA). However, as the signal-to-noise ratio is decreased, we uncover Lagrangian coherent structures (LCS) as the most compact representations which still allow the decoder to accurately reconstruct trajectories. This relationship offers increased interpretability to both PCA and LCS analysis, and helps to bridge the gap between two methods of flow analysis.

JFM classification

Nonlinear Dynamical Systems: Low-dimensional models Nonlinear Dynamical Systems: Pattern formation
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 927 , 25 November 2021 , A26
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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