Data-driven modal decomposition methods as feature detection techniques for flow problems: A critical assessment

B Begiashvili, N Groun, J Garicano-Mena… - Physics of …, 2023 - pubs.aip.org
Modal decomposition techniques are showing a fast growth in popularity for their wide range
of applications and their various properties, especially as data-driven tools. There are many …

[HTML][HTML] ModelFLOWs-app: data-driven post-processing and reduced order modelling tools

A Hetherington, A Corrochano… - Computer Physics …, 2024 - Elsevier
This article presents an innovative open-source software named ModelFLOWs-app, 1
written in Python, which has been created and tested to generate precise and robust hybrid …

[HTML][HTML] A dynamic mode decomposition technique for the analysis of non–uniformly sampled flow data

B Li, J Garicano-Mena, E Valero - Journal of Computational Physics, 2022 - Elsevier
Abstract A novel Dynamic Mode Decomposition (DMD) technique capable of handling non–
uniformly sampled data is proposed. As it is usual in DMD analysis, a linear relationship …

[HTML][HTML] Data repairing and resolution enhancement using data-driven modal decomposition and deep learning

A Hetherington, D Serfaty, A Corrochano, J Soria… - … Thermal and Fluid …, 2024 - Elsevier
This paper introduces a new series of methods which combine modal decomposition
algorithms, such as singular value decomposition and high-order singular value …

A data–driven sensibility tool for flow control based on resolvent analysis

E Lazpita, J Garicano-Mena, G Paniagua… - Results in …, 2024 - Elsevier
This study presents a novel application of data-driven resolvent analysis algorithm for flow
control. The objective is to identify key coherent structures connected to regions of the flow …

Implementation and Testing A Robust Data-repairing Method for Protection IEDs on Process Bus

MK Katoulaei, CM Adrah… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Modern power systems heavily rely on fast and reliable protection and control systems.
However, Sample Value (SV) packets transmitted via Process Bus protocols are susceptible …

A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved …

N Groun, M Villalba-Orero, L Casado-Martin… - arXiv preprint arXiv …, 2024 - arxiv.org
In this work, a data-driven, modal decomposition method, the higher order dynamic mode
decomposition (HODMD), is combined with a convolutional neural network (CNN) in order to …