Work

Fast uncertainty quantification of tracer distribution in the brain interstitial fluid with multilevel and quasi Monte Carlo

Year: 2020

Type: article

Abstract: Efficient uncertainty quantification algorithms are key to understand the propagation of uncertainty-from uncertain input parameters to uncertain output quantities-in high resolution mathematical models of brain physiology. Advanced Monte Carlo methods such as quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) have the potential to dramatically improve upon standard Monte Carlo (MC) methods, but their applicability and performance in biomedical applications is underexplored. In this paper, we design and apply QMC and MLMC methods to quantify uncertainty in a convection-diffusion model of tracer transport within the brain. We show that QMC outperforms standard MC simulations when the number of random inputs is small. MLMC considerably outperforms both QMC and standard MC methods and should therefore be preferred for brain transport models.

Source: International Journal for Numerical Methods in Biomedical Engineering

DOI: 10.1002/cnm.3412

Authors: Matteo Croci, Vegard Vinje, Marie E. Rognes

Institutions: Simula Research Laboratory, University of Oxford

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $4,200

Funders: Universitetet i Oslo, Norges Forskningsråd, Engineering and Physical Sciences Research Council, H2020 European Research Council