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SKM 2023 – wissenschaftliches Programm

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DY: Fachverband Dynamik und Statistische Physik

DY 17: Machine Learning in Dynamics and Statistical Physics I

Dienstag, 28. März 2023, 10:00–12:45, ZEU 160

10:00 DY 17.1 On-the-fly adaptive sparse grids for coupling high-fidelity and coarse-grained models — •Tobias Hülser, Sina Dortaj, and Sebastian Matera
10:15 DY 17.2 Reservoir Computing using Active Matter Model Systems: A Physics Viewpoint — •Mario U. Gaimann and Miriam Klopotek
10:30 DY 17.3 Machine Learning Percolation: Does it understand the physics? — •Djénabou Bayo, Andreas Honecker, and Rudolf A. Römer
10:45 DY 17.4 Bayesian deep learning for error estimation in the analysis of anomalous diffusion — •Henrik Seckler and Ralf Metzler
11:00 DY 17.5 A machine learned classical density functional for orientational correlations in the Kern-Frenkel model for patchy particles — •Alessandro Simon and Martin Oettel
  11:15 15 min. break
11:30 DY 17.6 Classification of Gel Networks using Graph Convolutional Neural Networks — •Matthias Gimperlein and Michael Schmiedeberg
11:45 DY 17.7 A 3-layer injection-locked multimode semiconductor laser neural network — •Elizabeth Robertson, Romain Lance, Anas Skalli, Xavier Porte, Janik Wolters, and Daniel Brunner
12:00 DY 17.8 Efficiently compressed time series approximations — •Paul Wilhelm and Marc Timme
  12:15 DY 17.9 The contribution has been withdrawn.
12:30 DY 17.10 Machine learning-based prediction of dynamical clustering in excited granular media — •Sai Preetham Sata, Dmitry Puzyrev, and Ralf Stannarius
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