PDRA in Computational Stochastics working with Michael B. Giles (Oxford).
+44 1865 615306
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Efficient white noise sampling and coupling for multilevel Monte Carlo
with non-nested meshes
SIAM/ASA Journal on Uncertainty Quantification issue 4 volume 6 page 1630-1655 (20 November 2018)
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields.
Fluids and barriers of the CNS issue 1 volume 16 page 32- (30 September 2019)
Complexity bounds on supermesh construction for quasi-uniform meshes
Full text available
Multilevel quasi Monte Carlo methods for elliptic PDEs with random field
coefficients via fast white noise sampling
Full text available
The main focus of my research is on multilevel Monte Carlo and finite element methods for the solution of PDEs with random coefficients and spatial white noise driven SPDEs. Currently, the application in mind is the quantification of uncertainty in brain fluid simulation.
Main research interests:
- Finite Element Methods for PDEs
- Multilevel Monte Carlo and Multilevel Quasi Monte Carlo
- Numerical Methods for Spatial White Noise driven SPDEs
- PDEs with Random Coefficients
- Uncertainty Quantification for SPDEs
- Mathematical Modelling of the Brain Physiology
- Numerical Linear Algebra
- Deflation for Complementarity Problems
Lay-man description of my DPhil thesis work.
FEniCS-based multilevel (quasi) Monte Carlo software for PDEs with random coefficients here.
Prizes, awards, and scholarships:
2015-2019: Oxford-Radcliffe Scholarship
Major / recent publications:
M. Croci, M. B. Giles, M. E. Rognes, P. E. Farrell - Multilevel quasi Monte Carlo methods for elliptic PDEs with random field coefficients via fast white noise sampling - submitted to SIAM/ASA Journal on Uncertainty Quantification - preprint.
M. Croci, P. E. Farrell - Complexity bounds on supermesh construction for quasi-uniform meshes - submitted to Journal of Computational Physics - preprint.