59 citations · 217 across the 22 of their papers we have counts for
24 papers
Operator-informed initialization for Fourier features physics-informed neural networks
Juan Molina, Paris Perdikaris, Mircea Petrache +2
Physics-Informed Neural Networks (PINNs) typically exhibit spectral bias, where some frequencies of the target function converge more slowly than others. In this work, we analyze t…
iLogMap: Geodesic Polar Coordinates Parameterization with the Magnetic Laplacian
Tomás Banduc, Simone Pezzuto, Francisco Sahli Costabal
Geodesic polar coordinates (GPCs) provide an intrinsic parameterization over curved surfaces, but their accurate estimation remains challenging, particularly in the presence of ani…
Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal +3
Atlas-based approaches allow high-quality, patient-specific shape reconstructions of cardiac anatomy from sparse and/or noisy data such as point clouds. However, these methods are…
WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
Felipe Álvarez Barrientos, Tomás Banduc, Isabeau Sirven +1
The contractile motion of the heart is strongly determined by the distribution of the fibers that constitute cardiac tissue. Strain analysis informed with the orientation of fibers…
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Vahidullah Taç, Amirhossein Amiri-Hezaveh, Manuel K. Rausch +3
We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation. Given a full-field measurement of the displ…
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
Efraín Magaña, Francisco Sahli Costabal, Wernher Brevis
An important tool for experimental fluids mechanics research is Particle Image Velocimetry (PIV). Several robust methodologies have been proposed to perform the estimation of veloc…
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Veronika Spieker, Hannah Eichhorn, Wenqi Huang +9
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time…
Ensemble learning of the atrial fiber orientation with physics-informed neural networks
Efraín Magaña, Simone Pezzuto, Francisco Sahli Costabal
The anisotropic structure of the myocardium is a key determinant of the cardiac function. To date, there is no imaging modality to assess in-vivo the cardiac fiber structure. We re…
An eikonal model with re-excitability for fast simulations in cardiac electrophysiology
Lia Gander, Rolf Krause, Francisco Sahli Costabal +1
Precision cardiology based on cardiac digital twins requires accurate simulations of cardiac arrhythmias. However, detailed models, such as the monodomain model, are computationall…
Understanding the dynamics of the frequency bias in neural networks
Juan Molina, Mircea Petrache, Francisco Sahli Costabal +1
Recent works have shown that traditional Neural Network (NN) architectures display a marked frequency bias in the learning process. Namely, the NN first learns the low-frequency fe…
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter +8
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application o…
Probabilistic learning of the Purkinje network from the electrocardiogram
Felipe Álvarez-Barrientos, Mariana Salinas-Camus, Simone Pezzuto +1
The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. H…
Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
Vahidullah Tac, Manuel K Rausch, Ilias Bilionis +2
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy fu…
Physics-informed neural networks for blood flow inverse problems
Jeremias Garay, Jocelyn Dunstan, Sergio Uribe +1
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known a…
Shape of my heart: Cardiac models through learned signed distance functions
Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal +6
The efficient construction of anatomical models is one of the major challenges of patient-specific in-silico models of the human heart. Current methods frequently rely on linear st…
Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields
Tabita Catalán, Matías Courdurier, Axel Osses +3
Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelera…
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
Vahidullah Tac, Manuel K. Rausch, Francisco Sahli-Costabal +1
We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy fun…
WarpPINN: Cine-MR image registration with physics-informed neural networks
Pablo Arratia López, Hernán Mella, Sergio Uribe +2
Heart failure is typically diagnosed with a global function assessment, such as ejection fraction. However, these metrics have low discriminate power, failing to distinguish differ…
-PINNs: physics-informed neural networks on complex geometries
Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
Physics-informed neural networks (PINNs) have demonstrated promise in solving forward and inverse problems involving partial differential equations. Despite recent progress on expa…
Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds
Simone Pezzuto, Paris Perdikaris, Francisco Sahli Costabal
We propose a method for identifying an ectopic activation in the heart non-invasively. Ectopic activity in the heart can trigger deadly arrhythmias. The localization of the ectopic…
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps
Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank +3
We propose FiberNet, a method to estimate \emph{in-vivo} the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac f…
Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification
Lia Gander, Simone Pezzuto, Ali Gharaviri +3
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace th…
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks
Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal +4
Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information ca…
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl +1
Machine learning techniques typically rely on large datasets to create accurate classifiers. However, there are situations when data is scarce and expensive to acquire. This is the…