# Talks

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Talks 21 to 30 of 663 | show all

Date | Time | Venue | Talk |
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05/08/24 | 12:00 pm | Am Schwarzenberg-Campus 3 (E), Room 3.074 and Zoom |
Ethics in Computational MathematicsProf. Max Kiener, Institute for Ethics in TechnologyThis 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: |

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 |

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. Zoomlink: |

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

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 termJulio 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: |

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 |

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

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

02/02/24 | 02:00 pm | Am Schwarzenberg-Campus 3 (E), Room 3.074 |
Dimension estimation [Studienarbeit]Michel Krispin |

01/24/24 | 01:00 pm | TUHH, Am Schwarzenberg-Campus 3 (E), Room 3.074 |
Sampling Theorems in Positive Definite Reproducing Kernel Hilbert Spaces [Bachelorarbeit]Lennart Ohlsen, Studiengang TM, Betreuer und Erstprüfer: Armin Iske |

* Talk within the Colloquium on Applied Mathematics