Search Results for author: Francisco Sahli Costabal

Found 12 papers, 6 papers with code

Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields

no code implementations11 Sep 2023 Vahidullah Tac, Manuel K Rausch, Ilias Bilionis, Francisco Sahli Costabal, Adrian Buganza Tepole

We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries.

Physics-informed neural networks for blood flow inverse problems

1 code implementation2 Aug 2023 Jeremias Garay, Jocelyn Dunstan, Sergio Uribe, Francisco Sahli Costabal

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 and scatter measurements are available.

Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields

no code implementations24 Jul 2023 Tabita Catalán, Matías Courdurier, Axel Osses, René Botnar, Francisco Sahli Costabal, Claudia Prieto

Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions.

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

1 code implementation22 Nov 2022 Pablo Arratia López, Hernán Mella, Sergio Uribe, Daniel E. Hurtado, Francisco Sahli Costabal

In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of the heart deformation.

Image Registration Landmark Tracking

$Δ$-PINNs: physics-informed neural networks on complex geometries

1 code implementation8 Sep 2022 Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris

We approximate the eigenfunctions as well as the operators involved in the partial differential equations with finite elements.

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

1 code implementation28 Jan 2022 Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto

The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse activation maps.

Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

no code implementations22 Feb 2021 Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause

In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation.

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

1 code implementation9 May 2019 Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado

In an application to cardiac electrophysiology, the multi-fidelity classifier achieves an F1 score, the harmonic mean of precision and recall, of 99. 6% compared to 74. 1% of a single-fidelity classifier when both are trained with 50 samples.

Active Learning BIG-bench Machine Learning +2

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