Hysteresis, Period Doubling, and Intermittency at High Prandtl Number in the Lorenz Equations
Fowler, A McGuinness, M Studies in Applied Mathematics volume 69 issue 2 99-126 (28 Oct 1983)
Blowing of polysilicon fuses
Lee, W Fowler, A Power, O Healy, S Browne, J (22 Mar 2011)
Movement of a Sessile Cell Colony
Fowler, A Winstanley, H Mathematical Proceedings of the Royal Irish Academy volume 112 issue 2 79-91 (01 Jan 2012)
Quasi-steady uptake and bacterial community assembly in a mathematical model of soil-phosphorus mobility
Moyles, I Donohue, J Fowler, A (10 Sep 2019)
The development of deep-ocean anoxia in a comprehensive ocean phosphorus model
Donohue, J Florio, B Fowler, A (19 Aug 2020)
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How do diseases spread and how can the analysis of data help us stop them? In this lecture, Christl Donnelly will draw lessons from past epidemics and endemic diseases, across livestock, wildlife, and human populations, to show how mathematical frameworks and statistical inference help unravel complex transmission systems. Click the link above for details.
Mon, 09 Feb 2026

15:30 - 16:30
L3

On blowup for wave maps with additive noise

Irfan Glogić
(Bielefeld University)
Abstract

We study a prototypical geometric wave equation, given by wave maps from the Minkowski space R 1+d into the sphere S d , under the influence of additive stochastic forcing, in all energy-supercritical dimensions d ≥ 3. In the deterministic setting, self-similar finite-time blowup is expected for large data, but remains open beyond perturbative regimes. We show that adding a non-degenerate Gaussian noise provokes finite-time blowup with positive probability for arbitrary initial data. Moreover, the blowup is governed by the explicit self-similar profile originally identified in the deterministic theory. Our approach combines local well-posedness for stochastic wave equations, a Da Prato-Debussche decomposition, and a stability analysis in self-similar variables. The result corroborates the conjecture that the self-similar blowup mechanism is robust and represents the generic large-data behavior in the deterministic problem.

This is joint work with M. Hofmanova and E. Luongo (Bielefeld)

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