SKM 2023 –
wissenschaftliches Programm
DY 17: Machine Learning in Dynamics and Statistical Physics I
Dienstag, 28. März 2023, 10:00–12:45, ZEU 160
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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
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10:15 |
DY 17.2 |
Reservoir Computing using Active Matter Model Systems: A Physics Viewpoint — •Mario U. Gaimann and Miriam Klopotek
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10:30 |
DY 17.3 |
Machine Learning Percolation: Does it understand the physics? — •Djénabou Bayo, Andreas Honecker, and Rudolf A. Römer
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10:45 |
DY 17.4 |
Bayesian deep learning for error estimation in the analysis of anomalous diffusion — •Henrik Seckler and Ralf Metzler
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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
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11:15 |
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15 min. break
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11:30 |
DY 17.6 |
Classification of Gel Networks using Graph Convolutional Neural Networks — •Matthias Gimperlein and Michael Schmiedeberg
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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
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12:00 |
DY 17.8 |
Efficiently compressed time series approximations — •Paul Wilhelm and Marc Timme
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12:15 |
DY 17.9 |
The contribution has been withdrawn.
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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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