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.
People |
ORCID iD |
Mike Giles (Principal Investigator) |
Publications
Cliffe K
(2011)
Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients
in Computing and Visualization in Science
Giles M
(2013)
Monte Carlo and Quasi-Monte Carlo Methods 2012
Teckentrup A
(2013)
Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients
in Numerische Mathematik
Giles M
(2014)
Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without Lévy area simulation
in The Annals of Applied Probability
Giles M
(2015)
Multilevel Monte Carlo Approximation of Distribution Functions and Densities
in SIAM/ASA Journal on Uncertainty Quantification
Lester C
(2015)
An adaptive multi-level simulation algorithm for stochastic biological systems.
in The Journal of chemical physics
Giles M
(2015)
Multilevel Monte Carlo methods
in Acta Numerica
Vidal-Codina F
(2015)
A model and variance reduction method for computing statistical outputs of stochastic elliptic partial differential equations
in Journal of Computational Physics
Lester C
(2016)
Extending the Multi-level Method for the Simulation of Stochastic Biological Systems.
in Bulletin of mathematical biology
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 |