activity
20222024
most citedLow-cost singular value decomposition with optimal sensor placement

5 citations · 18 across the 10 of their papers we have counts for

collaborators

10 papers

eess.IV2024

Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets

Andrés Bell-Navas, Nourelhouda Groun, María Villalba-Orero +3

Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering th…

cs.CE20241 cited

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

A. Hetherington, D. Serfaty, A. Corrochano +2

This paper introduces a new series of methods which combine modal decomposition algorithms, such as singular value decomposition and high-order singular value decomposition, and de…

cs.CE20235 cited

Low-cost singular value decomposition with optimal sensor placement

Ashton Hetherington, Soledad Le Clainche

This paper presents a new method capable of reconstructing datasets with great precision and very low computational cost using a novel variant of the singular value decomposition (…

cs.CE20234 cited

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

A. Hetherington, A. Corrochano, R. Abadía-Heredia +6

This article presents an innovative open-source software named ModelFLOWs-app, written in Python, which has been created and tested to generate precise and robust hybrid reduced or…

physics.flu-dyn20233 cited

Deep Learning combined with singular value decomposition to reconstruct databases in fluid dynamics

Paula Díaz, Adrián Corrochano, Manuel López-Martín +1

Fluid Dynamics problems are characterized by being multidimensional and nonlinear. Therefore, experiments and numerical simulations are complex and time-consuming. Motivated by thi…

physics.flu-dyn20232 cited

Hierarchical Higher-Order Dynamic Mode Decomposition for Clustering and Feature Selection

Adrián Corrochano, Giuseppe D'Alessio, Alessandro Parente +1

In this work, a new algorithm based on the application of higher-order dynamic mode decomposition (HODMD) is proposed for feature selection and variables clustering in reacting flo…