Linear and nonlinear dimensionality reduction from fluid mechanics to machine learning
MA Mendez - Measurement Science and Technology, 2023 - iopscience.iop.org
Dimensionality reduction is the essence of many data processing problems, including
filtering, data compression, reduced-order modeling and pattern analysis. While traditionally …
filtering, data compression, reduced-order modeling and pattern analysis. While traditionally …
Flow visualization: state-of-the-art development of micro-particle image velocimetry
Experimental flow visualization is a valuable tool for analyzing microfluidics and nanofluidics
in a wide variety of applications. Since the late 1990s, considerable advances in optical …
in a wide variety of applications. Since the late 1990s, considerable advances in optical …
UK COVID-19 lockdown: 100 days of air pollution reduction?
On the 23 March 2020, a country-wide COVID-19 lockdown was imposed on the UK. The
following 100 days saw anthropogenic movements quickly halt, before slowly easing back to …
following 100 days saw anthropogenic movements quickly halt, before slowly easing back to …
[HTML][HTML] Review of dimension reduction methods
Purpose: This study sought to review the characteristics, strengths, weaknesses variants,
applications areas and data types applied on the various Dimension Reduction techniques …
applications areas and data types applied on the various Dimension Reduction techniques …
Singular value decomposition of noisy data: noise filtering
BP Epps, EM Krivitzky - Experiments in Fluids, 2019 - Springer
The singular value decomposition (SVD) and proper orthogonal decomposition are widely
used to decompose velocity field data into spatiotemporal modes. For noisy experimental …
used to decompose velocity field data into spatiotemporal modes. For noisy experimental …
Data-driven identification of coherent structures in gas–solid system using proper orthogonal decomposition and dynamic mode decomposition
Spatiotemporal coherent structures are critical in quantifying the hydrodynamics of dense
gas–solid flows. In this study, two data-driven methods, proper orthogonal decomposition …
gas–solid flows. In this study, two data-driven methods, proper orthogonal decomposition …
Implications of the selection of a particular modal decomposition technique for the analysis of shallow flows
JE Higham, W Brevis, CJ Keylock - Journal of Hydraulic Research, 2018 - Taylor & Francis
This work deals with the capabilities of two synoptic modal decomposition techniques for the
identification of the spatial patterns and temporal dynamics of coherent structures in shallow …
identification of the spatial patterns and temporal dynamics of coherent structures in shallow …
Missing data recovery using data fusion of incomplete complementary data sets: A particle image velocimetry application
X Wen, Z Li, D Peng, W Zhou, Y Liu - Physics of Fluids, 2019 - pubs.aip.org
A data-fusion approach is reported to reconstruct missing data and is applied to particle
image velocimetry (PIV) measurements. This approach departs from the existing ones in that …
image velocimetry (PIV) measurements. This approach departs from the existing ones in that …
Using a proper orthogonal decomposition to elucidate features in granular flows
We apply proper orthogonal decomposition (POD) technique to analyze granular rheology
in a laboratory-scale pulsed-fluidized bed. POD allows us to describe the inherent dynamics …
in a laboratory-scale pulsed-fluidized bed. POD allows us to describe the inherent dynamics …
[HTML][HTML] Modification of modal characteristics in wakes of square cylinders with multi-scale porosity
JE Higham, A Vaidheeswaran, W Brevis, F Nicolleau… - Physics of …, 2021 - pubs.aip.org
Wake flows behind porous patches are complex and host several spatiotemporal features
associated with multi-scale excitation. This is in stark contrast to flow past a square cylinder …
associated with multi-scale excitation. This is in stark contrast to flow past a square cylinder …