title: Unraveling flow patterns through nonlinear manifold learning.
abstract: 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.
authors.affiliation: Department of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Brooklyn, New York, United States of America; Dipartimento di Ingegneria Civile, Edile e Ambientale, Sapienza University of Rome, Rome, Italy.
authors.forename: Flavia
authors.initials: F
authors.lastname: Tauro
authors.affiliation: Department of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Brooklyn, New York, United States of America; Dipartimento per l'Innovazione nei Sistemi Biologici, Agroalimentari e Forestali, University of Tuscia, Viterbo, Italy.
authors.forename: Salvatore
authors.initials: S
authors.lastname: Grimaldi
authors.affiliation: Department of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Brooklyn, New York, United States of America.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Porfiri
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019992:Databases as Topic; D007858:Learning; D017711:Nonlinear Dynamics; D012212:Rheology