7 citations · 16 across the 5 of their papers we have counts for
5 papers
A hybrid proper orthogonal decomposition and diffusion framework for reduced-order forecasting of turbulent flow dynamics
Rodrigo Abadia-Heredia, Xiangrui Zou, Manuel Lopez-Martin +2
Forecasting turbulent flow dynamics requires a balance between predictive fidelity and computational efficiency. Diffusion-based generative models can represent complex spatiotempo…
Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models
Rodrigo Abadía-Heredia, Adrián Corrochano, Manuel Lopez-Martin +1
This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applicat…
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…
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…
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…