661 citations · 794 across the 5 of their papers we have counts for
5 papers
Variational Autoencoding Neural Operators
Jacob H. Seidman, Georgios Kissas, George J. Pappas +1
Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and physical systems. A natur…
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…
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…
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Georgios Kissas, Yibo Yang, Eileen Hwuang +3
Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and…