The most robust representations of flow trajectories are Lagrangian coherent structures
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 28, 2021
- Source ID
- 10.1017/jfm.2021.768
Entities
People
- David H Richter
- Theodore MacMillan
Organizations
- Army Research Office
- Office of Naval Research