Multilevel Monte Carlo Methods for Elliptic Problems with Applications to Radioactive Waste Disposal

Lead Research Organisation: University of Oxford
Department Name: Mathematical Institute

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Publications

10 25 50
publication icon
Giles M (2018) Random Bit Quadrature and Approximation of Distributions on Hilbert Spaces in Foundations of Computational Mathematics

publication icon
Giles M (2014) Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without Lévy area simulation in The Annals of Applied Probability

publication icon
Giles M (2015) Multilevel Monte Carlo methods in Acta Numerica

publication icon
Giles M (2018) Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI in Statistics and Computing

publication icon
Giles M (2019) Random bit multilevel algorithms for stochastic differential equations in Journal of Complexity

publication icon
Giles M (2018) Monte Carlo and Quasi-Monte Carlo Methods

publication icon
Giles M (2018) Multilevel Estimation of Expected Exit Times and Other Functionals of Stopped Diffusions in SIAM/ASA Journal on Uncertainty Quantification

publication icon
Giles M (2013) Monte Carlo and Quasi-Monte Carlo Methods 2012

publication icon
Giles M (2015) Multilevel Monte Carlo Approximation of Distribution Functions and Densities in SIAM/ASA Journal on Uncertainty Quantification

publication icon
Giles M (2016) Monte Carlo and Quasi-Monte Carlo Methods

 
Description This project has demonstrated that the multilevel Monte Carlo method provides major improvements in the computational efficiency of Monte Carlo methods applied to the simulation of nuclear waste repositories.
Exploitation Route There is major potential for its use in the simulation of nuclear waste repositories, and also oil reservoir simulation.
Sectors Education,Energy
URL http://people.maths.ox.ac.uk/gilesm/mlmc.html
 
Description The mathematical approach we have developed has not yet been adopted by industry, although it is now being widely used within academia and both government and industry research labs.
First Year Of Impact 2011
Sector Aerospace, Defence and Marine,Education,Electronics,Financial Services, and Management Consultancy