Data-driven prediction in dynamical systems: recent developments
A Ghadami, BI Epureanu - Philosophical Transactions of …, 2022 - royalsocietypublishing.org
In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …
larger-scale systems in the majority of the grand societal challenges tackled in applied …
A review on turbulent and vortical flow analyses via complex networks
G Iacobello, L Ridolfi, S Scarsoglio - Physica A: Statistical Mechanics and …, 2021 - Elsevier
Turbulent and vortical flows are ubiquitous and their characterization is crucial for the
understanding of several natural and industrial processes. Among different techniques to …
understanding of several natural and industrial processes. Among different techniques to …
Comparing different nonlinear dimensionality reduction techniques for data-driven unsteady fluid flow modeling
Computational fluid dynamics (CFD) is known for producing high-dimensional
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …
Generalized Lagrangian coherent structures
S Balasuriya, NT Ouellette, II Rypina - Physica D: Nonlinear Phenomena, 2018 - Elsevier
The notion of a Lagrangian Coherent Structure (LCS) is by now well established as a way to
capture transient coherent transport dynamics in unsteady and aperiodic fluid flows that are …
capture transient coherent transport dynamics in unsteady and aperiodic fluid flows that are …
Graph convolutional networks applied to unstructured flow field data
Many scientific and engineering processes produce spatially unstructured data. However,
most data-driven models require a feature matrix that enforces both a set number and order …
most data-driven models require a feature matrix that enforces both a set number and order …
Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: a computation and data-driven approach
A physics-based data-driven computational framework for the quantitative analysis of vortex
kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows …
kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows …
Introducing OpenLPT: new method of removing ghost particles and high-concentration particle shadow tracking
We developed an open-source Lagrangian particle tracking (OpenLPT) based on the Shake-
the-Box (Schanz, Gesemann, and Schröder, Exp. Fluids 57.5, 2016) method. The source …
the-Box (Schanz, Gesemann, and Schröder, Exp. Fluids 57.5, 2016) method. The source …
[HTML][HTML] Network-based analysis of fluid flows: Progress and outlook
The network of interactions among fluid elements and coherent structures gives rise to the
incredibly rich dynamics of vortical flows. These interactions can be described with the use …
incredibly rich dynamics of vortical flows. These interactions can be described with the use …
Cluster-based hierarchical network model of the fluidic pinball–cartographing transient and post-transient, multi-frequency, multi-attractor behaviour
We propose a self-supervised cluster-based hierarchical reduced-order modelling
methodology to model and analyse the complex dynamics arising from a sequence of …
methodology to model and analyse the complex dynamics arising from a sequence of …
How sensitive are Lagrangian coherent structures to uncertainties in data?
A Badza, TW Mattner, S Balasuriya - Physica D: Nonlinear Phenomena, 2023 - Elsevier
Lagrangian coherent structures (LCSs) are time-varying entities which capture the most
influential transport features of a flow. These can for example identify groups of particles …
influential transport features of a flow. These can for example identify groups of particles …