4 citations · 4 across the 17 of their papers we have counts for
11 papers · 1 filter
Reduced-order modeling of a viscoelastic turbulent jet with hybrid machine learning models
Christian Amor, Adrián Corrochano, Marco Edoardo Rosti +1
Adding flexible polymers to a Newtonian solvent confers complex properties to the resulting solution. The additional complexity substantially increases the computational cost of nu…
A Critical Assessment of Pattern Comparisons Between POD and Autoencoders in Intraventricular Flows
Eneko Lazpita, Andrés Bell-Navas, Jesús Garicano-Mena +2
Understanding intraventricular hemodynamics requires compact and physically interpretable representations of the underlying flow structures, as characteristic flow patterns are clo…
Efficient Reduced Order Modeling Based on HODMD to Predict Intraventricular Flow Dynamics
Eneko Lazpita, Jesus Garicano-Mena, Soledad Le Clainche
Accurate and efficient modeling of cardiac blood flow is crucial for advancing data-driven tools in cardiovascular research and clinical applications. Recently, the accuracy and av…
Characterizing Intraventricular Flow Patterns via Modal Decomposition Techniques in Idealized Left Ventricle Models
Eneko Lazpita, Michael Neidlin, Jesus Garicano-Mena +1
Understanding the formation, propagation, and breakdown of the main vortex ring (VR) is essential for characterizing left ventricular (LV) hemodynamics, as its dynamics have been l…
Generative artificial intelligence and hybrid models to accelerate LES in reactive flows: Application to hydrogen/methane combustion
Xiangrui Zou, Rodrigo Abadia-Heredia, Laura Saavedra +3
With increasing emphasis on carbon neutrality, accurate and efficient combustion prediction has become essential for the design and optimization of new generation combustion system…
An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning
Rodrigo Abadía-Heredia, Manuel Lopez-Martin, Soledad Le Clainche
This work presents, to the best of the authors' knowledge, the first generalizable and fully data-driven adaptive framework designed to stabilize deep learning (DL) autoregressive…