70 citations · 70 across the 15 of their papers we have counts for
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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…
MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
Guillermo Barragán, Ashton Hetherington, Arindam Sengupta +3
We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal forecasting of high-dimensional unsteady systems. The meth…
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…
HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics
Guillermo Barragán, Ashton Hetherington, Rodrigo Abadía-Heredia +2
In this work we present a novel methodology that combines Higher Order Singular Value Decomposition (HOSVD) with Deep Learning (DL) techniques for super-resolution in computational…
Modeling heart flow dynamics using numerical simulations to identify the vortex ring: a practical guide
Eneko Lazpita, Andrea Mares, Pedro Quintero +2
In this study, we present a comprehensive numerical analysis of blood flow within human left ventricle models, with particular emphasis on optimizing simulation conditions to enhan…
Data-driven modal decomposition methods as feature detection techniques for flow problems: a critical assessment
Beka Begiashvili, Nourelhouda Groun, Jesús Garicano-Mena +2
Modal decomposition techniques are showing a fast growth in popularity for their good properties as data-driven tools. There are several modal decomposition techniques, yet Proper…