1.1k citations · 7.2k across the 70 of their papers we have counts for
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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…
Fast PDE-constrained optimization via self-supervised operator learning
Sifan Wang, Mohamed Aziz Bhouri, Paris Perdikaris
Design and optimal control problems are among the fundamental, ubiquitous tasks we face in science and engineering. In both cases, we aim to represent and optimize an unknown (blac…
Improved architectures and training algorithms for deep operator networks
Sifan Wang, Hanwen Wang, Paris Perdikaris
Operator learning techniques have recently emerged as a powerful tool for learning maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can…
Long-time integration of parametric evolution equations with physics-informed DeepONets
Sifan Wang, Paris Perdikaris
Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. I…
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Sifan Wang, Hanwen Wang, Paris Perdikaris
Deep operator networks (DeepONets) are receiving increased attention thanks to their demonstrated capability to approximate nonlinear operators between infinite-dimensional Banach…
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
Mohamed Aziz Bhouri, Paris Perdikaris
This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems. The pr…
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…
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs
Yibo Yang, Antoine Blanchard, Themistoklis Sapsis +1
We present a new type of acquisition functions for online decision making in multi-armed and contextual bandit problems with extreme payoffs. Specifically, we model the payoff func…