Welcome to the Department of Mathematical Sciences
Mathematical Sciences at Durham offers a unique blend of high quality teaching and research in pure mathematics, theoretical physics, numerical analysis, biomathematics, statistics and probability theory. We pride ourselves on combining world-leading research with commitment to the learning experience of our undergraduate and postgraduate students. Our core objective is to achieve and maintain academic excellence in an environment recognising diversity and a healthy gender balance as undeniable strengths, and to lead by example in our efforts to inspire our young mathematicians, students and researchers, to embrace a career that will continue to provide what attracted them to a Maths degree: the fun of mathematics, its sheer elegance and understated beauty, and the thrill of solving puzzles.
The curriculum and degree structure is frequently revisited and modernized to provide our students with the best possible mathematical and general skills, so that they adapt swiftly in a rapidly changing professional environment. For example we now have a new degree programme with a year in industry that will give interested students practical experience of problem solving in a commercial environment as well as enhance their employability. Read more...
Professor Anne Taormina
Head of Department
- BP Achievement Awards 2014-15
- Equality in Higher Education: Now? Sometime? Never?
- LMS Grace Chisholm Young Fellowship in Mathematics awarded to Emilie Dufresne
- Forecaster's predictive analytics engine developed by David Wooff and his team
- PhD student Thomai Tsiftsi wins SET for Britain competition
- Jens Funke and the 150 years of the London Mathematical Society
- Trevelyan teams up to offer Scholarship
- Equality, Diversity & Unconscious Bias Session
- HoD to join the LMS Women in Mathematics Committee
- Maths BP summer intern creates the buzz at Durham Risk Day
- Maths Hub A-level Revision Session
- Only Connect: Durham Maths Graduate Captains Team in BBC2 Quiz
- The Institute of Physics highlights research done in our Department
- Collingwood Lecture 2013 a big hit
- Durham Maths and the African Institute for Mathematical Sciences
Thursday 5 March 2015 @ 5:15pm in CLC013: Public Lecture by Professor Reidun Twarock (York Centre for Complex Systems Analysis). `Geometry: A secret weapon in the fight against viruses'
Thursday 26 February 2015 @ 4:00pm in CLC013: Collingwood Lecture 2015 by Professor Martin Hairer FRS, Fields Medallist 2014 (Warwick).`Taming Infinities'
Wednesday 11 June 2014 @ 2:00pm in CM101: Pascal Lecture 2014 by Professor Gary Gibbons FRS (Cambridge). `The solitons concept and gravity'
Thursday 30 January 2014 @ 4:30pm in W103: Collingwood Lecture 2014 by Professor Wendelin Werner, Fields Medallist 2006 (Zurich). `Randomness in the continuum'
Tuesday 5 November 2013 @ 4:00pm in CLC013: Collingwood Lecture 2013 by Professor Peter Higgs FRS (Edinburgh), introduced by Professor Steve Abel (Durham). `The electroweak symmetry breaking and the Higgs boson'. Nobel Prize winner’s visit shows Durham’s standing in Mathematics and Physics
ECS-Mathematical Sciences Energy Seminars: Measurement-based identification of power system dynamic model & Numerical Methods for Optimal Control
16 May 2012 14:00 in E240
Janusz Bialek - Measurement-based identification of power system dynamic model (or inverse-engineering approach to eigenanalysis).
Eigenanalysis is a standard tool to analyse power system dynamics whereby the response of a high-order dynamic system is represented as superposition of responses of first- and second-order systems (so-called modes) defined by eigenvalues of the system state matrix. Obviously to determine eigenvalues it is necessary to know the full system model (i.e. the state matrix). This talk will describe an attempt at inverse-engineering eigenanalysis when the unknown system model is extracted from measurements of system modes and mode shapes. Professional help from linear algebra mathematicians is required to explain unexpected results when it was possible to extract the system model from an incomplete set of measurements.
Max Jensen - Numerical Methods for Optimal Control
I begin this short talk reviewing classical results from optimal control theory to place the approach by Bellman into a wider context. I then describe why the numerical solution of the Bellman equation remains a challenging problem. I conclude with some examples to show how this approach has been used to problems in energy production and finance.
Mathematical modelling underlies much of energy engineering. At Durham, relevant engineering research ranges from power network reliability, economics and planning, through reliability analysis of generation units, to computational fluid dynamics models of wind and steam turbines. For this reason, ECS and Mathematical Sciences are organising a joint seminar series to explore opportunities for future collaborative research and grant proposals.
Each seminar will consist of a 20-30 minute talk from each discipline, followed by an extended discussion. While these seminars are open to any Durham researcher, the series is tightly focused on discovering topics for future external proposals between the Mathematical Sciences and ECS.
A selection of recent grants
- New homotopy-type invariants of knots (EPSRC, Andrew Lobb)
- Photospheric Driving of Non-Potential Coronal Magnetic Field Simulations (US Air Force, Anthony Yeates)
- SPOCK: Scientific Properties Of Complex Knots (Leverhulme Trust Research Programme Grant, Paul Sutcliffe)
- SPiN: Symmetry Principles in Nature (ERC Consolidator Grant, Mukund Rangamani)
- Utkin, L.V., Coolen, F.P.A. & Gurov, S.V. (2015). Imprecise inference for warranty contract analysis. Reliability Engineering & System Safety 138: 31-39.
- Guica. Monica & Ross, Simon F. (2015). Behind the geon horizon. Classical and Quantum Gravity 32(5): 055014.
- Miranda, Enrique, Troffaes, Matthias C. M. & Destercke, Sébastien (2015). A geometric and game-theoretic study of the conjunction of possibility measures. Information Sciences 298: 373–389.
- Coolen, F.P.A. & Coolen-Maturi, T. (2015). Predictive inference for system reliability after common-cause component failures. Reliability Engineering & System Safety 135: 27-33.
- Wang, B.X., Yu, K. & Coolen, F.P.A. (2015). Interval estimation for proportional reversed hazard family based on lower record values. Statistics & Probability Letters 98: 115-122.