59 citations · 217 across the 22 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
-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…
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…