Vorträge
Vorträge 1 bis 10 von 698 | Gesamtansicht
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Datum | Zeit | Ort | Vortrag |
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13.06.25 | 10:00 | Gebäude N, Raum 0007 |
For What the Bell Tolls* David Keyes, Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Saudi Arabia With today’s exascale computers requiring 20 to 40 MW and some cloud centers exceeding 100MW, with no slacking of demand in sight, computing is a nonnegligible factor in climate change. For the past three years, we have been finalists in the Gordon Bell Prize with computations that do more with less – that scale up while squeezing out operations and data transfers that do not ultimately impact application accuracy requirements. Scientific and engineering computing has a history of “oversolving” inherited from a period when its cost was small enough to neglect. Today’s market for computing hardware is driven by machine learning applications that are able to exploit lower precision arithmetic. Traditional computational science and engineering are therefore being reinvented to employ lower precision arithmetic and replacement of blocks of operator and field data by low-rank substitutes, where possible without impacting accuracy. We provide examples from various applications, including Gordon Bell Prize finalist research in 2022 in environmental statistics, in 2023 in seismic processing, and in 2024 in genomics and again in climate emulation. The last was awarded the 2024 Gordon Bell Prize in Climate Modeling. In this talk, we will elucidate the algorithmic “secret sauce” shared by these diverse applications for which the (Gordon) Bell tolls. ![]() |
10.06.25 | 09:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 |
Bachelorarbeit: Entrauschen von Lösungen der Maxey-Riley-Gatignol-Gleichung mittels maschinellem Lernen Durmus Alas ![]() |
14.05.25 | 12:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 und Zoom |
Pararell-in-Time Methods with an ML based coarse propagator Abdul Qadir Ibrahim Iterative parallel-in-time algorithms like Parareal can extend scaling beyond the saturation of purely spatial parallelization when solving initial value problems. Zoomlink: ![]() |
22.04.25 | 10:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 |
Masterarbeit: Simulation of Waves in a Wave Flume with Bathymetry Using the Euler Equations Christoph Zetek ![]() |
15.04.25 | 10:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 |
Bachelorarbeit: Physik-gestützte Gauß-Prozess-Regression Salva Iqbal ![]() |
14.04.25 | 09:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 |
Data-Driven Koopman Operator for the Maxey-Riley-Gatignol Equation [Bachelorarbeit] Argjent Zulfiu ![]() |
09.04.25 | 15:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 |
Counting relative to random sets Peter Allen, Department of Mathematics, London School of Economics Conlon and Gowers in 2016 described a general approach to proving sparse random analogues of extremal results in combinatorics, such as bounding the minimum and maximum number of triangles in any subgraph of G(n,p) with a given number of edges. The general part of this approach is a functional-analytic statement which, given a sparse setting, constructs a dense model. However, there is a condition which must be shown to hold with high probability to apply the dense model theorem. In Conlon and Gowers' work, there is a technical difficulty with the probabilistic part which leads to a rather involved proof, which applies only in a restricted setting (for example, they can handle triangles but not triangles with a pendant edge), and with quite poor bounds on 'high probability'. ![]() |
08.04.25 | 14:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 |
Intervallrechnungen im Problem der kollektiven Entscheidungsfindung Olga Zhukovska Ungefähre Themen des Vortrags: ![]() |
02.04.25 | 12:00 | Am Schwarzenberg-Campus 3 (E), Raum 3.074 und Zoom |
An introduction to human-centric and interactive machine learning Prof. Pierre-Alexander Murena, Schools of Study Mechanical Engineering Machine learning is often explored and developed in isolation from real-world contexts, primarily focusing on abstract tasks or benchmark datasets. However, when integrated into real-world applications, a crucial factor comes into play: human involvement. This talk will introduce human-centric machine learning, a growing field that emphasizes the need to account for human presence at every stage of the ML pipeline. I will present several practical examples illustrating how machine learning can be made more interactive and accessible to non-experts Zoomlink: ![]() |
27.03.25 | 10:00 | BigBlueButton und Zoom |
Numerical Simulation of Permanent Magnet Synchronous Machines [Projektarbeit] Felix Weiß Zoomlink: ![]() |
* Vortrag im Rahmen des Kolloquiums für Angewandte Mathematik