# Talks

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Talks 11 to 20 of 662 | show all

Date | Time | Venue | Talk |
---|---|---|---|

07/10/24 | 12:00 pm | Am Schwarzenberg-Campus 3 (E), Room 3.074 |
Zero-Shot Super-Resolution with Neural Operators [Bachelorarbeit]Melanie Gruschka |

07/04/24 | 11:00 am | Am Schwarzenberg-Campus 3 (E), Room 3.074 |
Large components of random graphsMatthias Lienau Inhomogeneous random graphs are a prominent tool for modeling real-world complex networks as they manage to capture key concepts such as the scale-free property. In this talk we will focus on two particular inhomogeneous random graph models, the Norros-Reittu model and the random connection model. The Norros-Reittu model uses a deterministic vertex set and can be seen as a generalisation of the famous Erdős–Rényi graph. The random connection model on the other hand yields a spatial random graph, which leads to natural clustering effects. Our main goal is to determine the asymptotic behaviour of the size of the largest component as the number of vertices or the size of the observation window, respectively, goes to infinity. For the Norros-Reittu model we also study asymptotics of other counting statistics. |

07/04/24 | 10:00 am | Am Schwarzenberg-Campus 3 (E), Room 3.074 |
Lower variance bounds and normal approximation of Poisson functionals with applications to stochastic geometryVanessa Trapp Lower bounds for variances are often needed to derive central limit theorems. In this talk, a generalised reverse Poincaré inequality is established, which provides a lower variance bound for Poisson functionals that depends on the difference operator of some fixed order. |

07/02/24 | 04:15 pm | Geomatikum, Besstraße 55, 20146 Hamburg, Hörsaal H5 |
Random vertex detection and the size of typical cellsMathias Sonnleitner, Universität Münster |

06/19/24 | 12:00 pm | Am Schwarzenberg-Campus 3 (E), Room 3.074 and Zoom |
Towards Hybrid Space-Time Finite Element/Deep Neural Network MethodsNils Margenberg Accurate flow simulations remain a challenging task. In this talk we discuss the use of deep neural networks for augmenting classical finite element simulations in fluid-dynamics. Zoomlink: |

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

06/07/24 | 01:00 pm | Am Schwarzenberg-Campus 3 (E), Room 3.074 |
Bachelorarbeit: Eine Python-C++ Kopplung für die Dyssol Software für ProzesssimulationenSarra Daknou |

06/05/24 | 12:00 pm | Am Schwarzenberg-Campus 3 (E), Room 3.074 and Zoom |
Smaller Stencil Preconditioners for RBF-FD discretized problemsMichael Koch Radial basis function finite difference (RBF-FD) discretization has recently emerged as an al- Zoomlink: |

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: |

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: |

* Talk within the Colloquium on Applied Mathematics