Maciej Maska - Learning by Confusion Identification and Characterization of Phase Transitions

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Maciej Maska
January 10, 2020
3:00PM - 4:00PM
Location
Physics Research Building Room 4138

Date Range
Add to Calendar 2020-01-10 15:00:00 2020-01-10 16:00:00 Maciej Maska - Learning by Confusion Identification and Characterization of Phase Transitions Seminar Series: Quantum Information   Friday, January 10 Physics Research Building, Room 4138 Maciej Maska, University of Silesia, Poland Title: Learning by Confusion Identification and Characterization of Phase Transitions   Abstract: The conventional methods of classification of phase transitions rely on identification of order parameters and singularities in the free energy and its derivatives. Recently, artificial neural networks have been proven to be an efficient tool to perform this task. It has been shown that properly trained neural network can precisely determine the critical temperature. The most popular approach is based on supervised learning, where the network is trained on a large number of labeled configurations. Another approach, "learning by confusion" is a combination of supervised and unsupervised learning. I will show how these methods can be used to identify phase transitions in simple condensed matter models. Additionally, I will demonstrate how the confusion scheme can be exploited to determine the order of a phase transition.   Event is free and open to the public. Physics Research Building Room 4138 Institute for Optical Science spectroscopy@osu.edu America/New_York public
Description

Seminar Series: Quantum Information

 

Friday, January 10

Physics Research Building, Room 4138

Maciej Maska, University of Silesia, Poland

Title: Learning by Confusion Identification and Characterization of Phase Transitions

 

Abstract: The conventional methods of classification of phase transitions rely on identification of order parameters and singularities in the free energy and its derivatives. Recently, artificial neural networks have been proven to be an efficient tool to perform this task. It has been shown that properly trained neural network can precisely determine the critical temperature. The most popular approach is based on supervised learning, where the network is trained on a large number of labeled configurations. Another approach, "learning by confusion" is a combination of supervised and unsupervised learning. I will show how these methods can be used to identify phase transitions in simple condensed matter models. Additionally, I will demonstrate how the confusion scheme can be exploited to determine the order of a phase transition.

 

Event is free and open to the public.