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
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
Vidal-Codina F
(2016)
An Empirical Interpolation and Model-Variance Reduction Method for Computing Statistical Outputs of Parametrized Stochastic Partial Differential Equations
in SIAM/ASA Journal on Uncertainty Quantification
Teckentrup A
(2013)
Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients
in Numerische Mathematik
Massa TB
(2023)
Fusel oil reaction in pressurized water: characterization and antimicrobial activity.
in 3 Biotech
Lester C
(2015)
An adaptive multi-level simulation algorithm for stochastic biological systems.
in The Journal of chemical physics
Lester C
(2016)
Extending the Multi-level Method for the Simulation of Stochastic Biological Systems.
in Bulletin of mathematical biology
Katsiolides G
(2018)
Multilevel Monte Carlo and improved timestepping methods in atmospheric dispersion modelling
in Journal of Computational Physics
Hironaka T
(2020)
Multilevel Monte Carlo Estimation of the Expected Value of Sample Information
in SIAM/ASA Journal on Uncertainty Quantification
Giles, MB
(2020)
Multivariate Algorithms and Information-Based Complexity
Giles M
(2019)
Multilevel Nested Simulation for Efficient Risk Estimation
in SIAM/ASA Journal on Uncertainty Quantification
Giles M
(2018)
Random Bit Quadrature and Approximation of Distributions on Hilbert Spaces
in Foundations of Computational Mathematics
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 methods
in Acta Numerica
Giles M
(2018)
Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI
in Statistics and Computing
Giles M
(2019)
Random bit multilevel algorithms for stochastic differential equations
in Journal of Complexity
Giles M
(2018)
Monte Carlo and Quasi-Monte Carlo Methods
Giles M
(2018)
Multilevel Estimation of Expected Exit Times and Other Functionals of Stopped Diffusions
in SIAM/ASA Journal on Uncertainty Quantification
Giles M
(2013)
Monte Carlo and Quasi-Monte Carlo Methods 2012
Giles M
(2015)
Multilevel Monte Carlo Approximation of Distribution Functions and Densities
in SIAM/ASA Journal on Uncertainty Quantification
Giles M
(2016)
Monte Carlo and Quasi-Monte Carlo Methods
Fang W
(2019)
Multilevel Monte Carlo method for ergodic SDEs without contractivity
in Journal of Mathematical Analysis and Applications
Fang W
(2020)
Adaptive Euler-Maruyama method for SDEs with nonglobally Lipschitz drift
in The Annals of Applied Probability
Fang W
(2022)
Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.
in Medical decision making : an international journal of the Society for Medical Decision Making
Fang W
(2018)
Monte Carlo and Quasi-Monte Carlo Methods
Croci M
(2021)
Multilevel Quasi Monte Carlo Methods for Elliptic PDEs with Random Field Coefficients via Fast White Noise Sampling
in SIAM Journal on Scientific Computing
| 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 |