Neural Operator Processes (NOPs) unify neural-process conditioning with neural-operator decoding for probabilistic full-field prediction from sparse joint input-output observations.
Approximate bayesian neural operators: Uncertainty quantification for parametric pdes.CoRR, abs/2208.01565, 2022
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
REEF-GP fits a Gaussian process to residuals of a frozen neural operator using its internal embeddings to deliver geometry-aware post-hoc uncertainty quantification for PDEs on unstructured domains.
A perturbation-based conformal prediction wrapper on Fourier Neural Operators yields narrower uncertainty bands than prior methods for 2D incompressible Navier-Stokes while preserving coverage in data-scarce regimes.
citing papers explorer
-
Neural Operator Processes for Probabilistic Operator Learning under Partial Observations
Neural Operator Processes (NOPs) unify neural-process conditioning with neural-operator decoding for probabilistic full-field prediction from sparse joint input-output observations.
-
Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning
REEF-GP fits a Gaussian process to residuals of a frozen neural operator using its internal embeddings to deliver geometry-aware post-hoc uncertainty quantification for PDEs on unstructured domains.
-
Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime
A perturbation-based conformal prediction wrapper on Fourier Neural Operators yields narrower uncertainty bands than prior methods for 2D incompressible Navier-Stokes while preserving coverage in data-scarce regimes.