4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 4 cited
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…
cs.MS2021
ProbNum: Probabilistic Numerics in Python
Jonathan Wenger, Nicholas Krämer, Marvin Pförtner +9
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential…