1.1k citations · 7.2k across the 70 of their papers we have counts for
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Random Weight Factorization Improves the Training of Continuous Neural Representations
Sifan Wang, Hanwen Wang, Jacob H. Seidman +1
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals. However, training them to captur…
-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…
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
Neural Operators offer a powerful, data-driven tool for solving parametric PDEs as they can represent maps between infinite-dimensional function spaces. In this work, we employ phy…
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
Arka Daw, Jie Bu, Sifan Wang +2
Despite the success of physics-informed neural networks (PINNs) in approximating partial differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution…
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Jacob H. Seidman, Georgios Kissas, Paris Perdikaris +1
Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid m…
Respecting causality is all you need for training physics-informed neural networks
Sifan Wang, Shyam Sankaran, Paris Perdikaris
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhi…
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…
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors
Yibo Yang, Georgios Kissas, Paris Perdikaris
We present a simple and effective approach for posterior uncertainty quantification in deep operator networks (DeepONets); an emerging paradigm for supervised learning in function…
Learning Operators with Coupled Attention
Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto +3
Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general bla…
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…