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Date Time Venue Talk
06/17/24 10:00 am Am Schwarzenberg-Campus 3 (E), Room 3.074 Applications of Gaussian Processes in Machine Learning [Bachelorarbeit]
Konstantin Zörner

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05/22/24 12:00 pm Am Schwarzenberg-Campus 3 (E), Room 3.074 and Zoom Numerical solution of singularly perturbed differential equations using Haar wavelet*
Vamika Rathi

I will be introducing myself formally and presenting my master's thesis, which concerns the study of numerical schemes for solving singularly perturbed differential equations, focusing on the Haar wavelet method.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

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05/08/24 12:00 pm Am Schwarzenberg-Campus 3 (E), Room 3.074 and Zoom Ethics in Computational Mathematics
Prof. Max Kiener, Institute for Ethics in Technology

This talk focuses on the mathematical models underlying reinforcement learning in artificial intelligence, particularly the reward functions in Markov Decision Processes. I argue that ethical principles related to well-being, safety, and equality are inherently reflected in these mathematical models. Building on this foundation, I then demonstrate how ethics can inform computational mathematics, while also addressing the challenges one encounters in this domain. Specifically, I discuss how the mathematical models behind reinforcement learning may rely on a distorted representation of ethics with respect to the determinacy and commensurability of ethical values.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

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04/30/24 09:30 am Am Schwarzenberg-Campus 3 (E), Room 3.074 Multilevel Solvers for Radial Basis Function Finite Difference Discretized Differential Equations (Bachelorarbeit)
Lasse Rippa

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04/03/24 12:00 pm Am Schwarzenberg-Campus 3 (E), Room 3.074 and Zoom Applying SDC methods to the next-generation of weather forecasting models*
Alex Brown, Met Office UK

In Numerical Weather Prediction and Climate modelling, computational efficiency and numerical accuracy are paramount. This work aims to implement time-parallel Spectral Deferred Correction (SDC) methods in LFRic-Atmosphere, the Met Office’s next-generation atmospheric model, designed to exploit the new supercomputers with improved scalability; the use of a quasi-uniform cubed-sphere mesh is integral to this, as is the underlying lowest-order compatible finite element spatial discretisation. LFRic-Atmosphere has an iterative semi-implicit time stepping structure with a Method of Lines finite-transport scheme using an explicit Runge-Kutta time discretisation. Time parallel SDC offers increased temporal accuracy with small computation cost, this could be utilised over the whole time discretisation, or to target a specific time discretised component.
I will present two approaches in this talk. The first approach is using serial SDC as the time discretisation of LFRic-Atmosphere’s finite-volume transport scheme. The second approach is using a serial IMEX SDC time stepper to compare to the semi-implicit time stepping structure in LFRic-Atmosphere. My initial work has explored both using the shallow water equations, I will present results from the standard shallow water test-cases.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

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03/22/24 10:00 am Am Schwarzenberg-Campus 3 (E), Room 3.074 Masterarbeit: Parameteridentifizierung mit Methoden des Maschinellen Lernens
Sahra Naser

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03/06/24 12:00 pm Am Schwarzenberg-Campus 3, Room H03 and Zoom Efficient numerical methods for the Maxey-Riley equations with Basset history term
Julio Urizarna

The Maxey-Riley Equation (MRE) models the motion of a finite-sized, spherical particle moving in a fluid. Applications using the MRE are, for example, the study of the spread of Coronavirus particles in a room, the formation of clouds and the so-called marine snow. The MRE is a second-order, implicit integro-differential equation with a singular kernel at initial time. For over 35 years, researchers used approximations and numerical schemes with high storage requirements or ignored the integral term, although its impact can be relevant. A major break-through was reached in 2019, when Prasath et al. mapped the MRE to a time-dependent Robin-type boundary condition of the 1D Heat equation, thus removing the requirement to store the full history. They provided an implicit integral form of the solution by using the so-called Fokas method that could be later solved with a numerical scheme and a nonlinear solver. While Prasath et al.’s method can deliver numerical solutions accurately, the need to evaluate nested integrals makes it computationally costly and it becomes impractical for computing trajectories of a large number of particles. In this talk, we present a new fourth order finite differences scheme and compare its accuracy and performance with Prasath et al’s method as well as other existing schemes. We then apply our method for the calculation of Lagrangian Coherent Structures, a large scale fluid structure, and point out for which cases, the approximations on the MRE have a considerable influence on these structures and the use of the full MRE models is relevant.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

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02/27/24 02:00 pm Am Schwarzenberg-Campus 3 (E), Room 3.074 Hawkes processes and their scaling limits for asset pricing models [Bachelorarbeit]
Niklas Jona Lohmann

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02/23/24 09:00 am Am Schwarzenberg-Campus 3 (E), Room 3.074 Bachelorarbeit: Surrogatmodelle für Lastsimulationen von Flügelklappen
Ana Vidya Moreno Molina

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02/14/24 12:00 pm Am Schwarzenberg-Campus 3 (E), Room 3.074 and Zoom Training Large Language Models on High-Performance Computing Systems
Chelsea John, Forschungszentrum Jülich

This presentation explores the intricacies of training large language models (LLM) on High-Performance Computing (HPC) systems, unveiling the key components, challenges, and optimizations involved in handling the computational demands of state-of-the-art language models. Delving into the nuances of model architecture, data preprocessing, and hyperparameter tuning, a comprehensive understanding of parallelization strategies, scalability challenges, and resource allocation will be given. Additionally, the talk touches on the implications for research, highlighting potential progress and future applications of LLMs.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

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* Talk within the Colloquium on Applied Mathematics