1 citations · 1 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Convergence for adaptive resampling of random Fourier features
Xin Huang, Aku Kammonen, Anamika Pandey +4
The machine learning random Fourier feature method for data in high dimension is computationally and theoretically attractive since the optimization is based on a convex standard l…
Hierarchical Importance Sampling for Estimating Occupation Time for SDE Solutions
Eya Ben Amar, Nadhir Ben Rached, Raul Tempone
This study considers the estimation of the complementary cumulative distribution function of the occupation time (i.e., the time spent below a threshold) for a process governed by…
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction Networks
Chiheb Ben Hammouda, Maksim Chupin, Sophia Münker +1
Stochastic reaction networks (SRNs) model stochastic effects for various applications, including intracellular chemical or biological processes and epidemiology. A typical challeng…
Forward Propagation of Low Discrepancy Through McKean-Vlasov Dynamics: From QMC to MLQMC
Nadhir Ben Rached, Abdul-Lateef Haji-Ali, Raúl Tempone +1
This work develops a particle system addressing the approximation of McKean-Vlasov stochastic differential equations (SDEs). The novelty of the approach lies in involving low discr…
Importance sampling for rare event tracking within the ensemble Kalman filtering framework
Nadhir Ben Rached, Erik von Schwerin, Gaukhar Shaimerdenova +1
In this work we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differentia…