TUHH / Institut für Mathematik / Vorträge Englische Flagge


Suchen | Vortragsverwaltung

Vorträge 1 bis 10 von 510 | Gesamtansicht

Nächste Seite Letzte Seite
Datum Zeit Ort Vortrag
29.11.21 15:00 Online (ZOOM) tba
Malte Braack, Christian-Albrechts-Universität zu Kiel

11.11.21 15:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Informationen zweiter Ordnung im Training neuronaler Netze [Masterarbeit]
Eva Lina Fesefeldt
08.11.21 15:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 & Zoom tba
Simon Campese

01.11.21 15:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Approximating Evolution Equations with Random Coefficients
Katharina Klioba

Solving evolution equations with random coefficients numerically requires discretizing in space, time and of random parameters. As numerical methods for all three discretisations are well-known, it is natural to ask under which conditions they can be combined. In this talk, we discuss this question with a special emphasis on preservation of strong convergence rates.

A common approach to spatial discretization consists of solving the weak formulation on finite-dimensional approximating spaces. We present a novel quantified version of the Trotter-Kato theorem in this setting, yielding rates of strong convergence under a joint condition on properties of the corresponding form and the approximating spaces.

This is joint work with Christian Seifert.
25.10.21 16:00 tba tba
Judith Angel
21.10.21 15:00 Zoom (see below for link) The quest for the cortical algorithm*
Dr. Helmut Linde, Merck KGaA, Darmstadt, Germany

How will the next generation of Artificial Intelligence (AI) look like? Comparing today's AI algorithms with biological intelligence, one of the most remarkable differences is the ability of the human brain to somehow understand the 'essence' of things: A small child can easily identify any type of object after having seen only a few examples or recognize a song even when played on different instruments or in a different key. In other words: Brains are able to create abstract concepts of real-world entities - and today's algorithms are not.

With today's AI largely being based on neuron models already invented by the mid of last century, I will argue that we should take a new look at the brain to find inspiration for the next generation of machine learning algorithms. Even though there is still only a very limited understanding of how the brain works computationally, I'll explain why there is hope that we can reverse-engineer some of its algorithmic principles and implement them in a computer. I'll explain why a highly interdisciplinary approach is required from neuroscience, computer science, mathematics and physics to make progress in this question.

The talk will be held on Zoom:
Meeting-ID: 868 3621 0324
Kenncode: 521014
21.10.21 11:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 und Zoom Non-autonomous Desch-Schappacher perturbations
Christian Budde, North-West University, Potchefstroom, South Africa

For many processes in sciences, the coefficients of the partial differential equation describing a dynamical system as well as the boundary conditions of it may vary with time. In such cases one speaks of non-autonomous (or time-varying) evolution equations. From an operator theoretical point of view one considers families of Banach space operators which depend on the time parameter and studies the associated non-autonomous abstract Cauchy problem. We consider time-dependent Desch-Schappacher perturbations of non-autonomous abstract Cauchy problems and apply our result to non-autonomous uniformly strongly elliptic differential operators on Lp -spaces. This is joint work with Christian Seifert (TUHH).
18.10.21 15:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 & Zoom Methods in Quantum Optimal Transport
Dennis Schmeckpeper

I will introduce myself and present the topic of my master thesis.

A fundamental principle in developing the Theory of Quantum Mechanics is to take
well-studied concepts from the Theory of Classical Mechanics and to define
analogues in the quantum mechanical setting.
One such important tool in Classical Mechanics is the theory of optimal
transport and in particular the Wasserstein distance.

In my thesis I studied the mathematical objects needed to translate
the concepts of the optimal transport problem to the realm of Quantum
Mechanics. In particular,
one wants to establish a relation between density matrices (trace-class operators
of trace one) and
probability measures. This can be done by the so-called
(generalized) Toeplitz operators and the (generalized) Husimi

After I give a brief introduction into both the Optimal Transport and Quantum
Mechanics I will introduce both
the Toeplitz operators and the Husimi transform and discuss some of their
30.09.21 16:00 TUHH, Gebäude D, 1.021 und Zoom Maker-Breaker Spiele über mehrere Runden [Bachelorarbeit TM]
Juri Barkey
30.09.21 15:00 Zoom Varianten von Toucher-Isolator Spielen auf Graphen [Bachelorarbeit TM]
Leon Speidel

* Vortrag im Rahmen des Kolloquiums für Angewandte Mathematik