4 citations · 9 across the 4 of their papers we have counts for
5 papers
Learning convolution operators on compact Abelian groups
Emilia Magnani, Ernesto De Vito, Philipp Hennig +1
We consider the problem of learning convolution operators associated to compact Abelian groups. We study a regularization-based approach and provide corresponding learning guarante…
Linearization Turns Neural Operators into Function-Valued Gaussian Processes
Emilia Magnani, Marvin Pförtner, Tobias Weber +1
Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differenti…
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…
Full history recursive multilevel Picard approximations for ordinary differential equations with expectations
Christian Beck, Martin Hutzenthaler, Arnulf Jentzen +1
We consider ordinary differential equations (ODEs) which involve expectations of a random variable. These ODEs are special cases of McKean-Vlasov stochastic differential equations…
Bayesian Filtering for ODEs with Bounded Derivatives
Emilia Magnani, Hans Kersting, Michael Schober +1
Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, o…