Dynamic mode decomposition (DMD) is a factorization and dimensionality reduction
technique for data sequences. In its most common form, it processes high-dimensional …

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …

THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …

We apply supervised machine learning techniques to a number of regression problems in
fluid dynamics. Four machine learning architectures are examined in terms of their …

Turbulent and vortical flows are ubiquitous and their characterization is crucial for the
understanding of several natural and industrial processes. Among different techniques to …

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 …

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 …

We propose a self-supervised cluster-based hierarchical reduced-order modelling
methodology to model and analyse the complex dynamics arising from a sequence of …

This article presents a data-driven model based on modal decomposition, applied to
approximate the low-order statistics of the spatially averaged wall-shear stress in a turbulent …

Particle tracking velocimetry (PTV) is a powerful technique for global and nonintrusive flow
field measurement, which shows a great potential to improve the spatial resolution …