Vorträge 1 bis 10 von 614 | Gesamtansicht
|17.10.23||10:00||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Bachelorarbeit: Ein auf maschinellem Lernen basierter Ansatz für "nudging" für "super-resolution"
|10.10.23||12:00||Am Schwarzenberg-Campus 3 (E), Raum 3.074 und Zoom||
Physics-Constrained Deep Learning for Downscaling and Emulation*
Paula Harder, Fraunhofer ITWM
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Two common ways to decrease computational efforts with DL are downscaling, the increase of the resolution directly on the predicted climate variables, and emulation, the replacement of model parts to achieve faster runs initially. Here, we look at several downscaling tasks and an aerosol emulation problem. While deep learning shows promising results it may not obey simple physical constraints, such as mass conservation or mass positivity. We tackle this by investigating both soft and hard constraining methodologies in different setups, showing that incorporating hard constraints can be beneficial for both downscaling and emulation problems.
|05.10.23||11:30||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Initial and Boundary Values for Evolutionary Equations
Andreas Buchinger, Institut für Angewandte Analysis, TU Bergakademie Feriberg
The theory of evolutionary equations, afforded by Rainer Picard (Dresden) et al., provides a well-posedness theorem applicable to a vast amount of linear PDEs including heat, wave and Maxwell's equations as well as equations including fractional derivatives and integrals. In this talk, I will discuss this well-posedness theorem in the autonomous case. I will show how to impose initial and boundary conditions on such evolutionary equations, and I will present a possible evolutionary approach to control theory for PDEs.
|27.09.23||12:00||Am Schwarzenberg-Campus 3 (E), Raum 3.074 und Zoom||
Harnessing the Power of GPUs: A Path to Efficiency and Excellence*
Prof. Sohan Lal, Massively Parallel Systems Group
Graphics Processing Units (GPUs), initially designed as accelerators for graphics applications, have revolutionized the computing landscape with their unparalleled computational prowess. Today, GPU-accelerated systems are present everywhere – for example, in our smartphones, cars, and supercomputers. GPU-accelerated systems are transforming the world in many ways, and several exciting possibilities, such as digital twins and precision medicine are on the horizon. While GPU-accelerated systems are desirable, their optimal utilization is crucial; otherwise, they can be very expensive in terms of power and energy consumption, which is not good as we aspire to reduce our carbon footprint. A single GPU can draw up to 700 watts, while GPU-powered supercomputers scale to the energy-hungry range of 1 to 10 megawatts.
|19.09.23||10:00||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Bachelorarbeit: Super-Resolution für die Flachwassergleichungen
|14.09.23||11:00||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Spectra of aperiodic Schrödinger operators [Masterarbeit]
Yasmeen Mai Hack, JMIM
|14.09.23||09:30||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Niveaumengen der Resolventennorm [Bachelorarbeit]
Daniel Wolf, TM
|28.08.23||09:30||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Erkennen von Botnetzen in Netzwerken [Bachelorarbeit]
|11.08.23||11:00||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Mondrian forests for classification [Bachelorarbeit]
Mohamed Yassine Daghfous
|09.08.23||11:00||Am Schwarzenberg-Campus 3 (E), Raum 3.074||
Random polytopes in polytopes
Matthias Reitzner, Universität Osnabrück
* Vortrag im Rahmen des Kolloquiums für Angewandte Mathematik