9 papers
Fast-Mixing Markov Chains without Gradients
Robert Kutri, Robert Scheichl
Most approaches for accelerating Markov chain mixing either rely on incorporating expensive geometric information in the proposals, or reduce the per-step cost of sampling via surr…
Robust spectral preconditioning for high-Péclet number convection-diffusion
Lukas Holbach, Peter Bastian, Robert Scheichl
We introduce a two-level hybrid restricted additive Schwarz (RAS) preconditioner for heterogeneous steady-state convection-diffusion equations at high Péclet numbers. Our construc…
Subspace accelerated measure transport methods for fast and scalable sequential experimental design, with application to photoacoustic imaging
Tiangang Cui, Karina Koval, Roland Herzog +1
We propose a novel approach for sequential optimal experimental design (sOED) for Bayesian inverse problems involving expensive models with high-dimensional unknown parameters. Thi…
Multigrid Monte Carlo Revisited: Theory and Bayesian Inference
Yoshihito Kazashi, Eike H. Müller, Robert Scheichl
Gaussian random fields play an important role in many areas of science and engineering. In practice, they are often simulated by sampling from a high-dimensional multivariate norma…
Exploiting Inexact Computations in Multilevel Monte Carlo and Other Sampling Methods
Josef MartÃnek, Erin Carson, Robert Scheichl
Multilevel sampling methods, such as multilevel and multifidelity Monte Carlo, multilevel stochastic collocation, or delayed acceptance Markov chain Monte Carlo, have become standa…
Optimal Spectral Approximation in the Overlaps for Generalized Finite Element Methods
Christian Alber, Peter Bastian, Moritz Hauck +1
In this paper, we study a generalized finite element method for solving second-order elliptic partial differential equations with rough coefficients. The method uses local approxim…