research
∙
04/01/2023
Multilevel CNNs for Parametric PDEs
We combine concepts from multilevel solvers for partial differential equ...
share
research
∙
02/06/2023
Convergence of adaptive Galerkin FEM for parametric PDEs with lognormal coefficients
Solving high-dimensional random parametric PDEs poses a challenging comp...
research
∙
12/22/2022
Less interaction with forward models in Langevin dynamics
Ensemble methods have become ubiquitous for the solution of Bayesian inf...
research
∙
03/17/2022
Low-rank Wasserstein polynomial chaos expansions in the framework of optimal transport
A unsupervised learning approach for the computation of an explicit func...
research
∙
12/02/2021
Adaptive non-intrusive reconstruction of solutions to high-dimensional parametric PDEs
Numerical methods for random parametric PDEs can greatly benefit from ad...
research
∙
05/19/2021
Efficient approximation of high-dimensional exponentials by tensornetworks
In this work a general approach to compute a compressed representation o...
research
∙
03/02/2021
Pricing high-dimensional Bermudan options with hierarchical tensor formats
An efficient compression technique based on hierarchical tensors for pop...
research
∙
08/17/2020
On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion
Convergence of an adaptive collocation method for the stationary paramet...
research
∙
08/10/2020
Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion
Transport maps have become a popular mechanic to express complicated pro...
research
∙
01/02/2020
