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

4 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

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…

cs.LG20232 cited

A predictive physics-aware hybrid reduced order model for reacting flows

Adrián Corrochano, Rodolfo S. M. Freitas, Alessandro Parente +1

In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. This algorithm is based on a dimensionality reduction using Proper Orth…

physics.flu-dyn2023

Mode selection in concentric jets. The steady-steady 1:2 resonant mode interaction with O(2) symmetry

Adrián Corrochano, Javier Sierra-Ausín, Juan Ángel Martin +2

In this article, a thorough characterization of the configuration composed by two concentric jets at a low Reynolds number is presented. The analysis comprises a layout with a wide…