4 citations · 4 across the 8 of their papers we have counts for
12 papers
Beyond exponential tilting: sharp asymptotics and improved importance sampling for rare events
Mingxin Li, Hans Reimann, Timo Schorlepp +1
A classical approach for importance sampling (IS) of a rare event is to tilt the measure using its large deviation minimizer, thereby shifting the proposal mean towards the event s…
Well-posed by Design: Learning Constitutive Laws from Velocity Data using Convex Neural Network Potentials
Gonzalo G. de Diego, Georg Stadler
Learning constitutive laws of complex fluids from velocity data (i.e. indirect observations) is a PDE-constrained inverse problem in which expressive neural parameterizations risk…
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.…
Optimal experimental design for passive imaging source problems
Christian Aarset, Thorsten Hohage, Georg Stadler
This work focuses on optimal experimental design (OED) methods for passive imaging. We adopt a Bayesian inverse problem framework for passive imaging source problems, primarily foc…
Physics-informed reservoir characterization from bulk and extreme pressure events with a differentiable simulator
Harun Ur Rashid, Mingxin Li, Aleksandra Pachalieva +2
Accurate characterization of subsurface heterogeneity is challenging but essential for applications such as reservoir pressure management, geothermal energy extraction and CO,…
Infinite-dimensional spherical-radial decomposition for probabilistic functions, with application to constrained optimal control and Gaussian process regression
Kewei Wang, Georg Stadler
The spherical-radial decomposition (SRD) is an efficient method for estimating probabilistic functions and their gradients defined over finite-dimensional elliptical distributions.…
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…
Non-Newtonian viscous fluid models with learned rheology accurately reproduce Lagrangian sea ice simulations
Gonzalo G. de Diego, Georg Stadler
Polar sea ice is crucial to Earth's climate system. Its dynamics also affect coastal communities, wildlife, and global shipping. Sea ice is typically modeled as a continuum fluid u…
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…
Modeling sea ice in the marginal ice zone as a dense granular flow with rheology inferred from discrete element model data
Gonzalo G. de Diego, Mukund Gupta, Skylar A. Gering +2
The marginal ice zone (MIZ) represents the periphery of the sea ice cover. In this region, the macroscale behavior of the sea ice results from collisions and enduring contact betwe…
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…