Universität Bonn

Hausdorff Colloquium 2025/26


Dates: Wednesday, October 15, 2025 - February 4, 2026

Organizers: Barbara Verfürth, Herbert Koch and Illia Karabash

Venue: Lipschitzsaal, Mathezentrum, Endenicher Allee 60, 53115 Bonn

Date

Hausdorff Tea

Hausdorff Colloquium

Graduate Colloquium




26.11.2025

 15:00

   


10.12.2025

15:00

   


17.12.2025

15:00

   


14.01.2026

15:00

   


21.01.2026

15:00

   


28.01.2026

15:00

   


04.02.2026

15:00

  

15:15
Speaker TBA
Title TBA




Abstracts

James Wright (University of Edingburgh, Schottland): "Recent progress in pointwise ergodic theory"

We survey recent results establishing pointwise almost everywhere convergence of ergodic averages. We will make connections between these developments and advances in quantitative bounds for polynomial progressions in dense sets of integers.

Michael Alexis (MI) "Calculus teachers hate him because of this one weird trick, find out why!!"

I’ll present the concept of dyadic decomposition, a simple but stupidly effective technique for quickly estimating various sums and integrals, all without computing a single anti-derivative. We’ll discuss topics ranging from the p-test in Calculus to the Calderon-Zygmund decomposition for estimating averaging operators and the Hardy-Littlewood maximal function.

Mario Ohlberger (University of Münster, Germany): "Reduced Order Surrogate Models for PDE-Constrained Optimization and Inverse Problems"

Classically, model order reduction for parameterized systems is based on a so-called offline phase, where reduced approximation spaces are constructed and the reduced parameterized
system is built, followed by an online phase, where the reduced system can be cheaply evaluated in a multi-query context. In this contribution, instead, we follow an active learning or
enrichment approach where a multi-fidelity hierarchy of reduced order models is constructed on-the-fly while exploring a parameterized system. To this end we focus on learning based
reduction methods in the context of PDE constrained optimization and inverse problems and evaluate their overall efficiency. We discuss learning strategies, such as adaptive enrichment
within a trust region optimization framework as well as a combination of reduced order models with machine learning approaches. Concepts of rigorous certification and convergence will be
presented, as well as numerical experiments that demonstrate the efficiency of the proposed approaches.

María Inés de Frutos Fernández (MI) "TBA"

TBA

Bjorn Poonen (UC Berkeley, USA): "TBA"

TBA

David Aretz (MPIM) "TBA"

TBA

Helge Holden (NTNU, Norway): TBA

TBA

Annabell Gros (IAM) "TBA"

TBA

Peter Hintz (ETH, Zürich, Swiss): TBA

TBA

TBA

TBA


Wird geladen