Francisco Sahli Costabal

According to our database, Francisco Sahli Costabal authored at least 18 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Δ-PINNs: Physics-informed neural networks on complex geometries.

Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris

Eng. Appl. Artif. Intell., January, 2024

Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance.

José Barahona, Francisco Sahli Costabal, Daniel E. Hurtado

Comput. Methods Programs Biomed., January, 2024

2023

WarpPINN: Cine-MR image registration with physics-informed neural networks.

Pablo Arratia López, Hernán Mella, Sergio Uribe, Daniel E. Hurtado, Francisco Sahli Costabal

Medical Image Anal., October, 2023

Probabilistic learning of the Purkinje network from the electrocardiogram.

Felipe Álvarez-Barrientos, Mariana Salinas-Camus, Simone Pezzuto, Francisco Sahli Costabal

CoRR, 2023

Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields.

Vahidullah Tac, Manuel K. Rausch, Ilias Bilionis, Francisco Sahli Costabal, Adrian Buganza Tepole

CoRR, 2023

Shape of my heart: Cardiac models through learned signed distance functions.

Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal, Rolf Krause, Angelo Auricchio, Gundolf Haase, Simone Pezzuto, Alexander Effland

CoRR, 2023

Physics-informed neural networks for blood flow inverse problems.

Jeremias Garay, Jocelyn Dunstan, Sergio Uribe, Francisco Sahli Costabal

CoRR, 2023

Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields.

Tabita Catalán, Matias Courdurier, Axel Osses, René M. Botnar, Francisco Sahli Costabal, Claudia Prieto

CoRR, 2023

Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations.

Vahidullah Tac, Manuel K. Rausch, Francisco Sahli Costabal, Adrian Buganza Tepole

CoRR, 2023

Benchmarks for physics-informed data-driven hyperelasticity.

Vahidullah Tac, Kevin Linka, Francisco Sahli Costabal, Ellen Kuhl, Adrian Buganza Tepole

CoRR, 2023

On the Accuracy of Eikonal Approximations in Cardiac Electrophysiology in the Presence of Fibrosis.

Lia Gander, Rolf Krause, Martin Weiser, Francisco Sahli Costabal, Simone Pezzuto

Proceedings of the Functional Imaging and Modeling of the Heart, 2023

The Fibrotic Kernel Signature: Simulation-Free Prediction of Atrial Fibrillation.

Francisco Sahli Costabal, Tomás Banduc, Lia Gander, Simone Pezzuto

Proceedings of the Functional Imaging and Modeling of the Heart, 2023

2022

Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps.

Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto

Eng. Comput., 2022

Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds.

Simone Pezzuto, Paris Perdikaris, Francisco Sahli Costabal

CoRR, 2022

2021

Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification.

Lia Gander, Simone Pezzuto, Ali Gharaviri, Rolf Krause, Paris Perdikaris, Francisco Sahli Costabal

CoRR, 2021

Automatically Polyconvex Strain Energy Functions using Neural Ordinary Differential Equations.

Vahidullah Tac, Francisco Sahli Costabal, Adrian Buganza Tepole

CoRR, 2021

Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks.

Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause

Proceedings of the Functional Imaging and Modeling of the Heart, 2021

2019

Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models.

Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado

CoRR, 2019