4 citations · 4 across the 8 of their papers we have counts for
7 papers · 1 filter
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.…