DPG Phi
Verhandlungen
Verhandlungen
DPG

Berlin 2024 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

DY: Fachverband Dynamik und Statistische Physik

DY 5: Machine Learning in Dynamics and Statistical Physics I

Monday, March 18, 2024, 09:30–13:00, BH-N 243

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
09:45 DY 5.2 Machine-learned Potentials for Vibrational Properties of Acene-based Molecular Crystals — •Shubham Sharma and Mariana Rossi
10:00 DY 5.3 Sampling free energies with deep generative models — •Maximilian Schebek, Michele Invernizzi, Frank Noé, and Jutta Rogal
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
  10:30 DY 5.5 The contribution has been moved to SOE 17.6/DY 28.6.
10:45 DY 5.6 Machine learning of a density functional for anisotropic patchy particles — •Alessandro Simon, Martin Oettel, and Georg Martius
11:00 DY 5.7 Systematic construction of velocity gradient models for turbulenceMaurizio Carbone, Vincent Peterhans, Alexander Ecker, and •Michael Wilczek
  11:15 15 min. break
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
11:45 DY 5.9 A Study of Quantum Non-Equilibrium Relations with Imaginary Path Integrals — •Jorge Castro, Eszter Pos, and Mariana Rossi
12:00 DY 5.10 Mean-field theories are simple for neural quantum states — •Fabian Ballar Trigueros, Tiago Mendes-Santos, and Markus Heyl
12:15 DY 5.11 Tensor-network-based reinforcement learning for quantum many-body systems — •Giovanni Cemin
12:30 DY 5.12 Derivative learning of tensorial quantities – Predicting infrared spectra from first principles — •Bernhard Schmiedmayer and Georg Kresse
12:45 DY 5.13 Stellar evolution forecasting with a timescale-adapted evolutionary coordinate and machine learning — •Kiril Maltsev and et al.
100% | Mobile Layout | Deutsche Version | Contact/Imprint/Privacy
DPG-Physik > DPG-Verhandlungen > 2024 > Berlin