Vorträge

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

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https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09

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https://meeting.rz.tuhh.de/rooms/xvo-f8c-vxq-oz9/join

The availability of real-time phase-resolved wave fields is crucial for the prediction and control of wave-induced motion of marine structures, a key parameter to extend the operational envelope and improve the optimal maneuvering of surface vessels. Using marine radar measurements of the ocean surface, the first step of the prediction problem is to reconstruct the surface dynamics (i.e. to extract the wave-related information from the measurements) in order to get the initial state of a wave model and propagate it in time to obtain the future wave conditions. Although fast and accurate models are available to propagate wave fields, the surface reconstruction — which can be seen as a nonlinear PDE-constrained optimization problem — is a very challenging task to perform in real time. In this talk, the specifics of the problem, which include the cost function to minimize, the wave model (PDE) and the optimization procedure, will be first presented with an emphasis on the use of parallel computation. Then, results will be shown for the simplified case of unidirectional waves. Finally, ideas for the efficient implementation (e.g. parallel in time) of the solver in the case of directional waves will be discussed.

In this talk, I will explore the application of Electrical Impedance Tomography (EIT) in chemical reactors, presenting a theoretical deduction of the underlying mathematical problem from its real-world context. The discussion will focus on the ill-posed nature of the EIT inverse problem and demonstrate how additional modeling assumptions can stabilize the reconstruction process.

The presentation explores ideas for developing an objective framework to evaluate reconstruction performance and seeks input on how to effectively incorporate physical domain information to enhance physics-based reconstruction approaches.

Zoomlink:
https://tuhh.zoom.us/j/81920578609?pwd=TjBmYldRdXVDT1VkamZmc1BOajREZz09