Forthcoming Seminars

Please note that the list below only shows forthcoming events, which may not include regular events that have not yet been entered for the forthcoming term. Please see the past events page for a list of all seminar series that the department has on offer.

Past events in this series
17 June 2019

Simple mathematical models have had remarkable successes in biology, framing how we understand a host of mechanisms and processes. However, with the advent of a host of new experimental technologies, the last ten years has seen an explosion in the amount and types of data now being generated. Increasingly larger and more complicated processes are now being explored, including large signalling or gene regulatory networks, and the development, dynamics and disease of entire cells and tissues. As such, the mechanistic, mathematical models developed to interrogate these processes are also necessarily growing in size and complexity. These detailed models have the potential to provide vital insights where data alone cannot, but to achieve this goal requires meeting significant mathematical challenges. In this talk, I will outline some of these challenges, and recent steps we have taken in addressing them.

  • Stochastic Analysis Seminar
18 June 2019
Andrew Gibbs

Highly oscillatory integrals arise in a range of wave-based problems. For example, they may occur when a basis for a boundary element has been enriched with oscillatory functions, or as part of a localised approximation to various short-wavelength phenomena. A range of contemporary methods exist for the efficient evaluation of such integrals. These methods have been shown to be very effective for model integrals, but may require expertise and manual intervention for
integrals with higher complexity, and can be unstable in practice.

The PathFinder toolbox aims to develop robust and fully automated numerical software for a large class of oscillatory integrals. In this talk I will introduce the method of numerical steepest descent (the technique upon which PathFinder is based) with a few simple examples, which are also intended to highlight potential causes for numerical instability or manual intervention. I will then explain the novel approaches that PathFinder uses to avoid these. Finally I will present some numerical examples, demonstrating how to use the toolbox, convergence results, and an application to the parabolic wave equation.

  • Numerical Analysis Group Internal Seminar


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