Classification of Spatio-Temporal Data via Asynchronous Sparse Sampling: Application to Flow Around a Cylinder

TL;DR

This paper introduces a method that uses sparse measurements and compressive sensing to classify and reconstruct high-dimensional, time-dependent flow data around a cylinder, enabling efficient analysis and modeling.

Contribution

It presents a novel approach combining asynchronous sparse sampling with compressive sensing for classification and reconstruction of spatio-temporal data.

Findings

Accurately classifies flow regimes with limited measurements

Reconstructs full spatio-temporal flow behavior from sparse data

Extends compressive sensing to dynamic, time-dependent systems

Abstract

We present a novel method for the classification and reconstruction of time dependent, high-dimensional data using sparse measurements, and apply it to the flow around a cylinder. Assuming the data lies near a low dimensional manifold (low-rank dynamics) in space and has periodic time dependency with a sparse number of Fourier modes, we employ compressive sensing for accurately classifying the dynamical regime. We further show that we can reconstruct the full spatio-temporal behavior with these limited measurements, extending previous results of compressive sensing that apply for only a single snapshot of data. The method can be used for building improved reduced-order models and designing sampling/measurement strategies that leverage time asynchrony.