Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs.
Emilia MagnaniNicholas KrämerRuna EschenhagenLorenzo RosascoPhilipp HennigPublished in: CoRR (2022)
Keyphrases
- decision theory
- dempster shafer
- partial differential equations
- morphological operators
- network architecture
- neural network
- inherent uncertainty
- bayesian networks
- level set
- maximum likelihood
- biologically plausible
- parametric models
- belief functions
- bayesian learning
- bayesian methods
- bio inspired
- bayesian inference
- image denoising
- expected utility
- probability theory
- robust optimization
- mathematical morphology
- image processing