<tt>PCE-Net</tt>
: High-Dimensional Surrogate Modeling for Learning Uncertainty
 Shustin, P Ubaru, S Zimoń, M Lu, S Kalantzis, V Horesh, L Avron, H SIAM/ASA Journal on Uncertainty Quantification volume 14 issue 1 168-196 (31 Mar 2026)

Talk about your thesis

The DPhil speaking event ‘Talk about your thesis’ will take place on the 20 March from 12 to 2 pm at the RSL. 

The event will feature 7 talks from DPhil students about their project, followed by a free pizza lunch.

Extended Regge Complex for Linearized Riemann-Cartan Geometry and Cohomology
 Christiansen, S Hu, K Lin, T Foundations of Computational Mathematics (01 Jan 2026)
Mon, 01 Jun 2026
16:30
L4

TBA

Nicos Kapouleas
(Brown University)
Abstract

TBA

Banner for event

Computers, Geometry and Einstein

Computers have long been useful for studying mathematical problems. But recently computer techniques have been used to prove new theorems in geometry, specifically related to the study of gravity through Einstein's theory of General Relativity. This Oxford Mathematics Public Lecture will describe these developments and what they might mean for the future.

Postdoctoral Research Associate in Topological Data Analysis (up to 2 posts)

We invite applications for up to two Postdoctoral Research Associates to work with Professors Ulrike Tillmann, Vidit Nanda and Heather Harrington on an exciting project in applied topology for data science with links to geometry and systems biology. These are full-time, fixed-term positions for 24 months funded by a Centre-to-Centre research grant from the EPSRC. The start-date for these positions are flexible, but September – October 2026 is preferred.

Collective dynamics on higher-order networks
 Battiston, F Bick, C Lucas, M Millán, A Skardal, P Zhang, Y Nature Reviews Physics (01 Jan 2026)
Fri, 06 Mar 2026
13:15
L6

Geometric and topological potentials driving self-assembly

Ivan Spirandelli
(University of Potsdam)
Abstract
The assembly of molecular building blocks into functional complexes is a central theme in biology and materials science. In this talk, we showcase the generative and thermodynamically predictive capabilities of a geometric model, the morphometric approach to solvation free energy, applied to spherical particles, tubes, and protein subunits. We demonstrate that this purely geometric description is sufficient to generate biologically relevant structural motifs and identify native nucleation states in simulation.
 
However, relying solely on local geometric fit often leads to optimization challenges. Molecular simulations frequently become trapped in local minima because the model lacks global structural information. To address this, we introduce a global bias based on persistent homology. By incorporating a weighted sum of total persistence as an active potential, we obtain an efficient simulation strategy, significantly increasing success rates. Integrating topological descriptions into energy functions offers a general strategy for overcoming kinetic barriers in molecular simulations, with potential applications in drug design, material development, and the study of complex self-assembly processes.
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