79 citations · 105 across the 7 of their papers we have counts for
9 papers · 1 filter
Learned Adaptive Multiresolution Diffusion Imaging
Christian Tantardini, Stig Rune Jensen, Roberto Di Remigio Eikås +1
Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are usually governed by fixed local…
Adaptive Multiresolution Diffusion Operators: A Variational Theory on Evolving Multiresolution Spaces
Christian Tantardini, Stig Rune Jensen, Roberto Di Remigio Eikås +1
We develop a variational framework for state-dependent diffusion on adaptive multiresolution representations in which the diffusion operator is generated by the adaptive state itse…
Goal-Oriented Adaptive Finite Element Multilevel Quasi-Monte Carlo
Joakim Beck, Yang Liu, Erik von Schwerin +1
The efficient approximation of quantities of interest derived from PDEs with lognormal diffusivity is a central challenge in uncertainty quantification. This paper targets a proble…
Goal-Oriented Adaptive Finite Element Multilevel Monte Carlo with Convergence Rates
Joakim Beck, Yang Liu, Erik von Schwerin +1
We present an adaptive multilevel Monte Carlo (AMLMC) algorithm for approximating deterministic, real-valued, bounded linear functionals that depend on the solution of a linear ell…
Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design
Joakim Beck, Ben Mansour Dia, Luis F. R. Espath +1
An optimal experimental set-up maximizes the value of data for statistical inferences and predictions. The efficiency of strategies for finding optimal experimental set-ups is part…
Multilevel Monte Carlo Acceleration of Seismic Wave Propagation under Uncertainty
Marco Ballesio, Joakim Beck, Anamika Pandey +3
We interpret uncertainty in a model for seismic wave propagation by treating the model parameters as random variables, and apply the Multilevel Monte Carlo (MLMC) method to reduce…
IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains
Joakim Beck, Lorenzo Tamellini, Raúl Tempone
This paper proposes an extension of the Multi-Index Stochastic Collocation (MISC) method for forward uncertainty quantification (UQ) problems in computational domains of shape othe…
Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain
Joakim Beck, Ben Mansour Dia, Luis FR Espath +2
In calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number…
A sparse-grid isogeometric solver
Joakim Beck, Giancarlo Sangalli, Lorenzo Tamellini
Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent…