Projects per year

## Personal profile

### Research interests

I am a member of the Numerical Analysis Group. My interests are in the design and analysis of efficient and robust parallel numerical methods for engineering and physical problems with heterogeneous material properties that vary over multiple scales. This is typical in energy and environmental applications, but also in material science and manufacturing. My research spans the whole range from the regularity analysis of solutions to the efficient parallel implementation of novel methods and their industrial application. I am particularly interested in multilevel and multiscale methods for partial differential equations with strongly varying and high contrast coefficients, in particular domain decomposition and multigrid methods, preconditioners for systems of PDEs, iterative eigensolvers, and multiscale discretisation techniques with applications in oil reservoir simulation, radioactive waste disposal, numerical weather and climate prediction, novel optical materials or composite materials.

More recently my particular focus has been on the interface between computational mathematics and statistics/probability. In most applications with heterogeneous material properties the coefficients are not known exactly. In fact, they are usually highly uncertain. One of the most popular ways to deal with uncertainty is stochastic modelling. However, most of the statistical tools for uncertainty quantification are either very inaccurate or computationally infeasible for typical engineering applications. Similar things can be said for data assimilation, for example in numerical weather prediction. My current research focusses mainly on two promising variants of the classical Monte Carlo method, namely multilevel Monte Carlo and quasi-Monte Carlo, which can provide highly accurate and efficient tools for uncertainty quantification. More recently we have extended the technology also to Bayesian inference by developing a multilevel Markov chain Monte Carlo method. The new methods are also of interest in time dependent problems with random noise (SDEs), e.g. in mathematical finance or in atmospheric dispersion modelling.

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## Projects 2009 2024

### Re-shaping the Test Pyramid

Scheichl, R. & Anaya-Izquierdo, K.

1/08/19 → 31/07/24

Project: Research council

### Sergey Dolgov Fellowship - Tensor Product Numerical Methods for High-Dimensional Problems in Probablility and Quantum Calculations

Engineering and Physical Sciences Research Council

1/01/16 → 31/12/18

Project: Research council

### IAA: Mathematical Investigation into the Methods used in Classical Quantitative Risk Assessment in the Energy and Process Industries

Shardlow, T., Budd, C., Jennison, C., Lindgren, F. & Scheichl, R.

Engineering and Physical Sciences Research Council

1/05/14 → 31/01/15

Project: Research council

### Multiscale Modelling of Aerospace Composites

Engineering and Physical Sciences Research Council

6/01/14 → 5/02/18

Project: Research council

## Research Output 2002 2019

### A hybrid Alternating Least Squares - TT Cross algorithm for parametric PDEs

Dolgov, S. & Scheichl, R., 2019, In : SIAM/ASA Journal on Uncertainty Quantification. 7, 1, p. 260-291 32 p.Research output: Contribution to journal › Article

### Analysis of quasi-Monte Carlo methods for elliptic eigenvalue problems with stochastic coefficients

Gilbert, A. D., Graham, I. G., Kuo, F. Y., Scheichl, R. & Sloan, I. H., 1 Aug 2019, In : Numerische Mathematik. 142, 4, p. 863-915 53 p.Research output: Contribution to journal › Article

### Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies

Detommaso, G., Dodwell, T. & Scheichl, R., 2019, In : SIAM/ASA Journal on Uncertainty Quantification. 7, 1, p. 93-116Research output: Contribution to journal › Article

### Analysis of circulant embedding methods for sampling stationary random fields

Graham, I. G., Kuo, F. Y., Nuyens, D., Scheichl, R. & Sloan, I. H., 2018, In : SIAM Journal on Numerical Analysis (SINUM). 56, 3, p. 1871-1895 25 p.Research output: Contribution to journal › Article

### A Stein variational Newton method

Detommaso, G., Cui, T., Marzouk, Y., Spantini, A. & Scheichl, R., 8 Jun 2018, (Accepted/In press)*Advances in Neural Information Processing Systems (NIPS) 2018.*

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

## Thesis

## Development Of A Performance-Portable Framework For Atomistic Simulations

Author: Saunders, W. R., 13 Feb 2019Supervisor: Mueller, E. (Supervisor), Parker, S. (Supervisor), Grant, R. (Supervisor) & Scheichl, R. (Supervisor)

Student thesis: Doctoral Thesis › PhD

## Multilevel Monte Carlo Methods and Uncertainty Quantication

Author: Teckentrup, A., 19 Jun 2013Supervisor: Scheichl, R. (Supervisor)

Student thesis: Doctoral Thesis › PhD

## Multi Level Monte Carlo Methods for Atmospheric Dispersion Modelling

Author: Cook, S., 22 Nov 2013Supervisor: Scheichl, R. (Supervisor) & Mueller, E. (Supervisor)

Student thesis: Doctoral Thesis › MPhil

## Numerical computation of band gaps in photonic crystal fibres

Author: Norton, R., 1 Sep 2008Supervisor: Scheichl, R. (Supervisor)

Student thesis: Doctoral Thesis › PhD

## Numerical solution of weather and climate systems

Author: Buckeridge, S., 31 Dec 2010Supervisor: Scheichl, R. (Supervisor)

Student thesis: Doctoral Thesis › PhD