Back to search results

Theoretical Analysis of Numerical Schemes for Stochastic (Partial) Differential Equations

University of Leeds - School of Mathematics

Qualification Type: PhD
Location: Leeds
Funding for: UK Students, Self-funded Students
Funding amount: See advert
Hours: Full Time
Placed On: 25th July 2019
Expires: 24th October 2019
Value: This project is open to self-funded students and is eligible for funding from the School of Mathematics Scholarships, EPSRC Doctoral Training Partnerships, and the Leeds Doctoral Scholarships.
Number of awards: 1
Deadline: Applications accepted all year round

Contact Dr Elena Issoglio to discuss this project further informally.

Project description

The theory and numerics of stochastic differential equations (SDEs) are well understood in the context of equations with `regular' coefficients. An important effort is currently being made in the field, in the attempt to go beyond the canonical setup. However, the challenge is huge, and results are often very specialised and not easily extendable to different equations.

This project aims at developing a solid theoretical analysis of numerical schemes for backward SDEs and stochastic partial differential equations (SPDEs) whose coefficients have very low regularity (typically elements in the class of Schwarz distributions).

As an example, the type of SPDEs mentioned above features naturally in physical problems modelled by transport equations in porous media, like water flowing through porous rocks: in this case the velocity of the flow is modified at the level of individual molecules, because the size of the water molecules is comparable to that of rock's pores. This can be mathematically modelled by taking the velocity as a very `rough' function of space (e.g., Schwarz distributions).

A starting point for this work would be as follows. We should consider sequences of regularised versions of the stochastic equation under study (i.e., involving a mollification of the `rough' coefficients). This approach is natural but hardly trivial because the convergence (and convergence rate) of the scheme will be strongly depending on a clever choice of the regularising sequence and on the norms that one adopts on the relevant functional spaces.

Current theoretical work of Dr Issoglio on these kinds of backward SDEs and SPDEs as well as some early results on numerical methods for SDEs can guide the start of the project. The potential outcomes of such study are likely to be of interest to the wide community of researchers working in stochastic analysis and PDE theory. Keywords: numerical methods, stochastic differential equations, BSDEs, SPDEs, irregular coefficients

Entry requirements

Applications are invited from candidates with or expecting a minimum of a UK upper second class honours degree (2:1) in Mathematics or a related discipline, or equivalent, and/or a Masters degree in a relevant subject.

How to apply

Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is the 'Theoretical analysis of numerical schemes for stochastic (partial) differential equations’ as well as Dr Elena Issoglio as your proposed supervisor.

If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates.

We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:

Subject Area(s):


PhD tools
More PhDs from University of Leeds

Show all PhDs for this organisation …

More PhDs like this
Join in and follow us

Browser Upgrade Recommended has been optimised for the latest browsers.

For the best user experience, we recommend viewing on one of the following:

Google Chrome Firefox Microsoft Edge