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11:00 |
AKPIK 3.1 |
A surrogate model for graphene-based conductor materials and the creation of an ontology-based digital twin — •Fabian Teichert, Philipp Schulze, Florian Fuchs, Martin Stoll, and Jörg Schuster
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11:00 |
AKPIK 3.2 |
Estimating sliding drop width using recurrent neural networks — •Sajjad Shumaly, Fahimeh Darvish, Xiaomei Li, Oleksandra Kukharenko, Werner Steffen, Yanhui Guo, Hans-Jürgen Butt, and Rüdiger Berger
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11:00 |
AKPIK 3.3 |
Performance comparison of heteroscedastic and homoscedastic noise models in Bayesian optimization — •Tatu Linnala, Armi Tiihonen, Matthias Stosiek, Milica Todorović, and Patrick Rinke
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11:00 |
AKPIK 3.4 |
Prospects of hybrid atomic-photonic neural networks for neuromorphic computing — •Mingwei Yang, Elizabeth Robertson, Kilian Junicke, Lina Jaurigue, Kathy Lüdge, and Janik Wolters
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11:00 |
AKPIK 3.5 |
Optical data processing for machine learning on board of satellites — •Inna Kviatkovsky, Okan Akyüz, Elizabeth Robertson, Mingwei Yang, Felix Kübler, José Diez López, Enrico Stoll, and Janik Wolter
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11:00 |
AKPIK 3.6 |
Unraveling Chronic Disease Relationships: A Comparative Analysis of Clustering Algorithms on the DHS 2019-2021 Indian Dataset — •Jannis Demel, Anna Nitschke, Carlos Brandl, Jonathan Berthold, and Matthias Weidemüller
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11:00 |
AKPIK 3.7 |
Phase retrieval by a conditional Wavelet Flow: applications to near-field X-ray holography — •Ritz Aguilar, Yunfan Zhang, Anna Willmann, Erik Thiessenhusen, Johannes Dora, Johannes Hagemann, Andre Lopes, Imke Greving, Berit Zeller-Plumhoff, Markus Osenberg, Michael Bussmann, and Jeffrey Kelling
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11:00 |
AKPIK 3.8 |
Coupling experiment and simulation through a digital infrastructure for materials science — •Marian Bruns, Jan Janßen, Tilmann Hickel, and Jörg Neugebauer
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11:00 |
AKPIK 3.9 |
Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning — •Daniel Köglmayr and Christoph Räth
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