3 papers
math.NA2025
A convergent adaptive finite element stochastic Galerkin method based on multilevel expansions of random fields
Markus Bachmayr, Martin Eigel, Henrik Eisenmann +1
The subject of this work is an adaptive stochastic Galerkin finite element method for parametric or random elliptic partial differential equations, which generates sparse product p…
cs.LG2024
Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures
Charles Miranda, Janina Schütte, David Sommer +1
We sample from a given target distribution by constructing a neural network which maps samples from a simple reference, e.g. the standard normal distribution, to samples from the t…
math.NA2024
Generative modeling with low-rank Wasserstein polynomial chaos expansions
Robert Gruhlke, Martin Eigel
A new Wasserstein multi-element polynomial chaos expansion (WPCE) is proposed, which is inspired by recent advances in computational optimal transport for estimating Wasserstein di…