Speaker: Prof. Maciej Maśka
Affiliation: Department of Theoretical Physics, University of Silesia, Poland
Simulation of exotic phases and emergent phenomena is a hot topic in condensed matter physics research, but conventional computing methods are reaching their limits: increasing number of particles leads to exponential growth of the corresponding state space and the available computer memory quickly becomes too small. Therefore, new computational approaches, which can efficiently deal with this huge amount of data, are required. State-of-the-art machine learning techniques seem to be the answer to at least some of these problems. Researchers are already re-purposing existing machine-learning algorithms to “learn” features of phases of matter, just as algorithms learn to recognize features in a photograph. With this knowledge, one can adapt them to handle other complex problems arising in condensed matter, especially in cases where quantum mechanics plays a role.
In the talk I will give an introduction to the general idea of machine learning and its application to problems in condensed matter physics. In a more detailed way I will discuss how machine learning techniques can be used to identify phase transitions and to speed up Monte Carlo simulations.
Seminar language: English
Chairman: Ireneusz Weymann
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