Unraveling flow patterns through nonlinear manifold learning

Flavia Tauro, Salvatore Grimaldi, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review


From climatology to biofluidics, the characterization of complex flows relies on computationally expensive kinematic and kinetic measurements. In addition, such big data are difficult to handle in real time, thereby hampering advancements in the area of flow control and distributed sensing. Here, we propose a novel framework for unsupervised characterization of flow patterns through nonlinear manifold learning. Specifically, we apply the isometric feature mapping (Isomap) to experimental video data of the wake past a circular cylinder from steady to turbulent flows. Without direct velocity measurements, we show that manifold topology is intrinsically related to flow regime and that Isomap global coordinates can unravel salient flow features.

Original languageEnglish (US)
Article numbere91131
JournalPloS one
Issue number3
StatePublished - Mar 10 2014

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • General


Dive into the research topics of 'Unraveling flow patterns through nonlinear manifold learning'. Together they form a unique fingerprint.

Cite this