4 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
Transform before linearizing: robust Newton methods for singular -Laplace and -Stokes equations
Gonzalo G. De Diego, Fortino Garcia, Georg Stadler +1
For , the -Laplace equation and its -Stokes generalization are difficult to solve numerically. Newton's method converges rapidly only close to the solution, with itera…
Amortized low-rank approximation for hyperparameter marginalization in PDE-governed Bayesian inverse problems
Sonia Reilly, Georg Stadler
This paper addresses the efficient solution of hierarchical Bayesian inverse problems with a high- or infinite-dimensional parameter field and a moderate number of hyperparameters.…
Learning parameter-dependent shear viscosity from data, with application to sea and land ice
Gonzalo G. de Diego, Georg Stadler
Complex physical systems which exhibit fluid-like behavior are often modeled as non-Newtonian fluids. A crucial element of a non-Newtonian model is the rheology, which relates inne…
A note on generating Voronoi cells with a given size distribution
Georg Stadler, Gonzalo G. De Diego
This note describes a simple method to draw random points such that the cells of the corresponding Voronoi tesselation (approximately) satisfy a desired size distribution, for inst…
Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems
Abhijit Chowdhary, Shanyin Tong, Georg Stadler +1
We study the sensitivity of infinite-dimensional Bayesian linear inverse problems governed by partial differential equations (PDEs) with respect to modeling uncertainties. In parti…