Berlin 2024 –
wissenschaftliches Programm
DY 19: Machine Learning in Dynamics and Statistical Physics II (joint session DY/SOE)
Dienstag, 19. März 2024, 09:30–13:00, BH-N 243
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09:30 |
DY 19.1 |
Pareto-Based Selection of Data-Driven Ordinary Differential Equations — •Gianmarco Ducci, Karsten Reuter, and Christoph Scheurer
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09:45 |
DY 19.2 |
Accurate Memory Kernel Extraction from Discretized Time-Series Data — •Lucas Tepper
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10:00 |
DY 19.3 |
Coarse-graining non-equilibrium systems with machine learning: from conceptual challenges to new approaches — •Patrick Egenlauf and Miriam Klopotek
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10:15 |
DY 19.4 |
Statistical criteria for the prediction of dynamical clustering in granular gases — •Sai Preetham Sata, Dmitry Puzyrev, and Ralf Stannarius
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10:30 |
DY 19.5 |
Excitability and Memory in a Time-Delayed Optoelectronic Neuron — •Jonas Mayer Martins, Svetlana V. Gurevich, and Julien Javaloyes
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10:45 |
DY 19.6 |
Anisotropic diffusion analysis in confined geometries — •Kevin Höllring, Andreas Baer, Nataša Vučemilović-Alagić, David M. Smith, and Ana-Sunčana Smith
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11:00 |
DY 19.7 |
Data assimilation of cardiac dynamics by means of adjoint optimization — •Inga Kottlarz, Sebastian Herzog, Patrick Vogt, Stefan Luther, and Ulrich Parlitz
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11:15 |
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15 min. break
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11:30 |
DY 19.8 |
Collective Variables for Neural Networks — •Konstantin Nikolaou, Samuel Tovey, Sven Krippendorf, and Christian Holm
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11:45 |
DY 19.9 |
Fluctuating weight dynamics and loss landscape in deep linear networks — •Markus Gross
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12:00 |
DY 19.10 |
Loss is More: Exploring the weight space of a perceptron via enhanced sampling techniques — •Margherita Mele, Roberto Menichetti, Alessandro Ingrosso, and Raffaello Potestio
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12:15 |
DY 19.11 |
Emergent oscillating dimensionality transformations in deep learning — •Pascal de Jong, Felix J. Meigel, and Steffen Rulands
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12:30 |
DY 19.12 |
Investigating the Evolution of Fisher Information for Neural Network Dynamics — •Marc Sauter, Samuel Tovey, Konstantin Nikolaou, and Christian Holm
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12:45 |
DY 19.13 |
Near-zero-cost post-training uncertainties for deep learning architectures — •Filippo Bigi, Sanggyu Chong, Michele Ceriotti, and Federico Grasselli
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