Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

Emilia Magnani, Nicholas Krämer, Runa Eschenhagen +2

Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of t…