32 citations · 58 across the 16 of their papers we have counts for
20 papers
Deep learning methods for stochastic Galerkin approximations of random domain problems
Fabio Musco, Andrea Barth
This work considers strong and weak stochastic Galerkin approximations of random domain problems for the elliptic random partial differential equation (PDE). The random domain prob…
Deep learning methods for stochastic Galerkin approximations of elliptic random PDEs
Fabio Musco, Andrea Barth
This work considers stochastic Galerkin approximations of linear elliptic partial differential equations (PDEs) with stochastic forcing terms and stochastic diffusion coefficients,…
Complexity analysis of quasi continuous level Monte Carlo
Cedric Aaron Beschle, Andrea Barth
Continuous level Monte Carlo is an unbiased, continuous version of the celebrated multilevel Monte Carlo method. The approximation level is assumed to be continuous resulting in a…
Quasi continuous level Monte Carlo for random elliptic PDEs
Cedric Aaron Beschle, Andrea Barth
This paper provides a framework in which multilevel Monte Carlo and continuous level Monte Carlo can be compared. In continuous level Monte Carlo the level of refinement is determi…
On properties and applications of Gaussian subordinated Lévy fields
Robin Merkle, Andrea Barth
We consider Gaussian subordinated Lévy fields (GSLFs) that arise by subordinating Lévy processes with positive transformations of Gaussian random fields on some spatial domain $\ma…
Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient
Andrea Barth, Robin Merkle
General elliptic equations with spatially discontinuous diffusion coefficients may be used as a simplified model for subsurface flow in heterogeneous or fractured porous media. In…