Applications of AAA rational approximation
Nakatsukasa, Y Trefethen, L Acta Numerica
Mon, 26 Jan 2026

17:00 - 18:00
L1

Enhancing Wind Energy Using Unsteady Fluid Mechanics

Prof. John Dabiri
(California Institute of Technology, USA)
Further Information
Abstract

This talk will describe recent studies of how time-dependent, unsteady flow physics can be exploited to improve the performance of energy harvesting systems such as wind turbines. A theoretical analysis will revisit the seminal Betz derivation to identify the role of unsteady flow from first principles. Following will be a discussion of an experimental campaign to test the predictions of the theoretical model. Finally, a new line of research related to turbulence transition and inspired by the work of T. Brooke Benjamin will be introduced.

 

Wed, 03 Dec 2025

16:00 - 17:00
L6

Letting AI untie the Knots

Ludovico Morellato
(Università degli Studi di Padova)
Abstract
In Knot Theory, one of the main interests is understanding when two knots are equivalent: even if they look completely different, one could actually be continuously deformed into the other.
Our main tool for this purpose are the topological invariants associated to a knot. However, computing them is not in general an easy task: it boils down to make a sequence of choices, a rather difficult work for us human. This is why, in recent years, mathematicians have begun using AI-driven solutions to compute these invariants, hoping that machines can identify patterns within the apparent chaos of possibilities.
In this talk, we are going to see how to compute two fundamental invariants, namely Unknotting Number and Slice Genus, with the aid of a Reinforcement Learning (RL) agent. We will start with the basic definitions from Knot Theory and Deep Learning, focusing on concepts rather than technical details, with the ultimate goal of understanding what RL is and how we can exploit it.
Adaptive-Robust Portfolio Optimisation
Bhudisaksang, T Cartea, Á Sanchez Betancourt, L Mathematics and Financial Economics
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
Lee, J James, L Choi, S Caron, F Proceedings of Machine Learning Research volume 89 758-767 (01 Jan 2019)

We invite applications for a Postdoctoral Research Associate (PDRA) to join the EPSRC Hub on the Mathematical and Computational Foundations of Artificial Intelligence. One PDRA will be recruited to work within one of, or across, the four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning through Stochastic Control (a brief description of each these is as follows (additional details are in the further particulars): 

Thu, 04 Dec 2025

12:00 - 13:00
C5

Flowing to Free Boundary Minimal Surfaces

Christopher Wright
(Mathematical Institute - University of Oxford)
Abstract

In this talk, I will discuss an approach to free boundary minimal surfaces which comes out of recent work by Struwe on a non-local energy, called the half-energy. I will introduce the gradient flow of this functional and its theory in the already studied case of disc type domains, covering existence, uniqueness, regularity and singularity analysis and highlighting the striking parallels with the theory of the classical harmonic map flow. Then I will go on to present new work, joint with Melanie Rupflin and Michael Struwe, which extends this theory to all compact surfaces with boundary. This relies upon combining the above ideas with those of the Teichmüller harmonic map flow introduced by Rupflin and Topping.

Fast Construction on a Restricted Budget
Frieze, A Krivelevich, M Michaeli, P Random Structures and Algorithms volume 67 issue 4 (29 Dec 2025)
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