Mon, 08 Dec 2025
11:00
11:00
L4
Renormalisation of the Gross-Neveu model in two dimensions à la Duch
Martin Peev
Abstract
I will discuss the paper "Construction of Gross-Neveu model using Polchinski flow equation" by Pawel Duch (https://arxiv.org/abs/2403.18562).
Fri, 13 Mar 2026
11:00 -
12:00
L4
Stop abusing Turing
Dr Thomas Woolley
(Dept of Maths Cardiff University)
Abstract
Everything you have been taught about Turing patterns is wrong! (Well, not everything, but qualifying statements tend to weaken a punchy first sentence). Turing patterns are universally used to generate and understand patterns across a wide range of biological phenomena. They are wonderful to work with from a theoretical, simulation and application point of view. However, they have a paradoxical problem of being too easy to produce generally, whilst simultaneously being heavily dependent on the details. In this talk I demonstrate how to fix known problems such as small parameter regions and sensitivity, but then highlight a new set of issues that arise from usually overlooked issues, such as boundary conditions, initial conditions, and domain shape. Although we’ve been exploring Turing’s theory for longer than I’ve been alive, there’s still life in the old (spotty) dog yet.
Fri, 06 Mar 2026
11:00 -
12:00
L4
Identifiability of stochastic and spatial models in mathematical biology
Dr Alexander Browning
(Dept of Mathematics University of Melbourne)
Abstract
Effective application of mathematical models to interpret biological data and make accurate predictions often requires that model parameters are identifiable. Requisite to identifiability from a finite amount of noisy data is that model parameters are first structurally identifiable: a mathematical question that establishes whether multiple parameter values may give rise to indistinguishable model outputs. Approaches to assess structural identifiability of deterministic ordinary differential equation models are well-established, however tools for the assessment of the increasingly relevant stochastic and spatial models remain in their infancy.
I provide in this talk an introduction to structural identifiability, before presenting new frameworks for the assessment of stochastic and partial differential equations. Importantly, I discuss the relevance of our methodology to model selection, and more the practical and aptly named practical identifiability of parameters in the context of experimental data. Finally, I conclude with a brief discussion of future research directions and remaining open questions.
Fri, 27 Feb 2026
11:00 -
12:00
L4