Berlin 2024 –
scientific programme
DY 5: Machine Learning in Dynamics and Statistical Physics I
Monday, March 18, 2024, 09:30–13:00, BH-N 243
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09:30 |
DY 5.1 |
Active-Learning Training of Accurate Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials — •Kisung Kang, Thomas A. R. Purcell, Christian Carbogno, and Matthias Scheffler
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09:45 |
DY 5.2 |
Machine-learned Potentials for Vibrational Properties of Acene-based Molecular Crystals — •Shubham Sharma and Mariana Rossi
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10:00 |
DY 5.3 |
Sampling free energies with deep generative models — •Maximilian Schebek, Michele Invernizzi, Frank Noé, and Jutta Rogal
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10:15 |
DY 5.4 |
Generative deep neural networks for topological defects and their microstructure reconstruction in two-dimensional spin systems — •Kyra Klos, Karin Everschor-Sitte, and Friederike Schmid
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10:30 |
DY 5.5 |
The contribution has been moved to SOE 17.6/DY 28.6.
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10:45 |
DY 5.6 |
Machine learning of a density functional for anisotropic patchy particles — •Alessandro Simon, Martin Oettel, and Georg Martius
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11:00 |
DY 5.7 |
Systematic construction of velocity gradient models for turbulence — Maurizio Carbone, Vincent Peterhans, Alexander Ecker, and •Michael Wilczek
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11:15 |
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15 min. break
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11:30 |
DY 5.8 |
Quantum Phase Transitions with Neural Network Quantum States and a Lee-Yang Method — •Pascal M. Vecsei, Jose L. Lado, and Christian Flindt
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11:45 |
DY 5.9 |
A Study of Quantum Non-Equilibrium Relations with Imaginary Path Integrals — •Jorge Castro, Eszter Pos, and Mariana Rossi
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12:00 |
DY 5.10 |
Mean-field theories are simple for neural quantum states — •Fabian Ballar Trigueros, Tiago Mendes-Santos, and Markus Heyl
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12:15 |
DY 5.11 |
Tensor-network-based reinforcement learning for quantum many-body systems — •Giovanni Cemin
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12:30 |
DY 5.12 |
Derivative learning of tensorial quantities – Predicting infrared spectra from first principles — •Bernhard Schmiedmayer and Georg Kresse
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12:45 |
DY 5.13 |
Stellar evolution forecasting with a timescale-adapted evolutionary coordinate and machine learning — •Kiril Maltsev and et al.
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