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Datum Zeit Ort Vortrag
07.07.23 15:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Extraktion strukturierter Daten aus deutschen Personalausweisen [Projektarbeit]
Anton Majboroda
05.07.23 12:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 tba
Hendrik Ranocha

21.06.23 12:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 tba
Benedict Philippi

20.06.23 15:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Vergleich verschiedener Verfahren der Dimensionsreduktion [Projektarbeit]
Tom Ahlgrimm
12.06.23 11:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Ein alternativer Ansatz zu bilateralen Filtern [Masterarbeit]
Michael Koch, Studiengang TM
07.06.23 12:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Machine Learning the Trajectories of the Maxey-Riley Equation
Leon Schlegel

Since we now have implemented an efficient solver for the Maxey-Riley equation, we can generate a lot of trajectory data. This data could be used to train a neural network, which can predict the trajectories given a starting postion. Because the dynamics are governed by an integro-differential equation, the future path of a trajectory depends on the whole past. This characteristic could be handled using recurrent neural networks.
I will show how the network performs on different velocity fields. There are cases where the model does a great job in the prediction, but there are still many problems to discuss and it will be interesting to hear some thoughts.
Finally I will show a network architecture that combines a Verlet integrator with a recurrent neural network.
24.05.23 12:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Challenges and Opportunities in Medical Image Reconstruction
Tobias Knopp

Tomographic imaging is an essential tool in medical diagnostics, allowing diseases to be detected much earlier
than would be possible from external observations alone. The aim is to determine a function representing the inner
of the human body from external measurements only so that the procedure is non-invasive and not harmful.
Determining this function, or in practice an appropriately discretized form, involves solving an
inverse problem, which is often ill-posed and must be solved for noisy measurements. In this talk, an
overview of different image reconstruction challenges and ways to address them algorithmically is given.
We also sketch possibilities that arise and allow for multi-contrast image reconstruction from only single
17.05.23 12:00 Am Schwarzenberg-Campus 3 (E), Raum 3.074 Hierarchical Block Structures for the Preconditioning of Saddle Point Problems with H-Matrix Decompositions
Jonas Grams

Fluid flow problems can be modelled by the Navier-Stokes, or Oseen equations. Their discretization results in saddle point problems. These systems of equations are typically very large and need to be solved iteratively. Standard (block-) preconditioning techniques for saddle point problems rely on an approximation of the Schur complement. Such an approximation can be obtained by a hierarchical matrix (H-Matrix) LU-decomposition for which the Schur complement is computed explicitly. The computational complexity of this computation depends, among other things, on the hierarchical block structure of the involved matrices. However, widely used techniques do not consider the connection between the discretization grids for the velocity field and the pressure, respectively. Thus, a problem dependent hierarchical block structure for the FEM discretization of the gradient operator is presented. The block structure of the corresponding saddle point matrix block is improved by considering the connection between the two involved grids.Numerical results will show that the improved block structure allows for a faster computation of the Schur complement, the bottleneck for the set-up of the H-Matrix LU-decomposition.
10.05.23 13:15 Am Schwarzenberg-Campus 4 (D), Raum 1.025 Extension of Linear Functions Onto Multivectors Using Geometric Algebra [Bachelorarbeit]
Alexander Busch
10.05.23 12:00 D 1.025 A mathematical introduction to quantum computing
Professor Martin Kliesch, Institute for Quantum-Inspired and Quantum Optimization

The first part of the presentation provides an introduction to quantum mechanics and quantum algorithms. In the second part, I will present an overview of the research at the new TUHH institute on the topic (see www.tuhh.de/quantum) and explain the mathematical aspects of it.