661 citations · 794 across the 5 of their papers we have counts for
3 papers · 1 filter
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…