4 citations · 4 across the 20 of their papers we have counts for
22 papers
Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications
Arindam Sengupta, Paul Jeanney, Ricardo Vinuesa +2
Urban flow and air-quality simulations generate high-dimensional datasets describing velocity and pollutant transport across multiple spatial, temporal, and physical-variable dimen…
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 temporal deep learning framework for calibration of low-cost air quality sensors
Arindam Sengupta, Tony Bush, Ben Marner +2
Low-cost air quality sensors (LCS) provide a practical alternative to expensive regulatory-grade instruments, making dense urban monitoring networks possible. Yet their adoption is…
Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations
Abhijeet Vishwasrao, Francisco Giral, Mahmoud Golestanian +8
Flow physics and more broadly physical phenomena governed by partial differential equations (PDEs), are inherently continuous, high-dimensional and often chaotic in nature. Traditi…
CardioMOD-Net: A Modal Decomposition-Neural Network Framework for Diagnosis and Prognosis of HFpEF from Echocardiography Cine Loops
Andrés Bell-Navas, Jesús Garicano-Mena, Antonella Ausiello +3
Introduction: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages, making early diagnosis a…
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…
Generative Urban Flow Modeling: From Geometry to Airflow with Graph Diffusion
Francisco Giral, Álvaro Manzano, Ignacio Gómez +2
Urban wind flow modeling and simulation play an important role in air quality assessment and sustainable city planning. A key challenge for modeling and simulation is handling the…
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…
Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
Paul Jeanney, Ashton Hetherington, Shady E. Ahmed +4
This paper presents an innovative Reduced-Order Model (ROM) for merging experimental and simulation data using Data Assimilation (DA) to estimate the "True" state of a fluid dynami…
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…
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…
Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
Arindam Sengupta, Rodrigo Abadía-Heredia, Ashton Hetherington +2
Accurate modeling of the complex dynamics of fluid flows is a fundamental challenge in computational physics and engineering. This study presents an innovative integration of High-…
A low cost singular value decomposition based data assimilation technique for analysis of heterogeneous combustion data
Prajith Pillai, Ashton Hetherington, Laura Saavedra Sago +1
This article applies low-cost singular value decomposition (lcSVD) for the first time, to the authors knowledge, on combustion reactive flow databases. The lcSVD algorithm is a nov…
LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements
Ashton Hetherington, Javier López Leonés, Soledad Le Clainche
This article introduces a novel methodology that integrates singular value decomposition (SVD) with a shallow linear neural network for forecasting high resolution fluid mechanics…
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…
EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach
Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin +4
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the field faces sign…
Review on vortex dynamics in the left ventricle as an early diagnosis marker for heart diseases and its treatment outcomes
Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena +1
The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of…
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation
Francisco Giral, Ignacio Gómez, Ricardo Vinuesa +1
This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs), designed to adapt in real time to dynamic changes caused…
A Novel Data-Driven Method for the Analysis and Reconstruction of Cardiac Cine MRI
Nourelhouda Groun, Maria Villalba-Orero, Enrique Lara-Pezzi +3
Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed informatio…
Eigensolution analysis of immersed boundary method based on volume penalization: applications to high-order schemes
Jiaqing Kou, Aurelio Hurtado-de-Mendoza, Saumitra Joshi +2
This paper presents eigensolution and non-modal analyses for immersed boundary methods (IBMs) based on volume penalization for the linear advection equation. This approach is used…