Topic: 
Intelligente Energieversorgungsnetze 
Date: 
08.11.21 
Time: 
16:15 
Place: 
H4 
Guest: 

FH Bielefeld 

Abstract: 

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Topic: 
Random matrices, spin glasses, and machine learning 
Date: 
23.07.21 
Time: 
16:15 
Place: 
ZOOM/Konferenzschaltung 
Guest: 

Oxford University 

Abstract: 
I will describe some problems relating to machine learning and their connections to random matrix theory and spin glasses. These connections give a mathematical framework for understanding in qualitative terms the effectiveness of certain algorithms that are important in machine learning, but developing them into precise models remains a major challenge. I will reflect on the different roles played by models in computer science and physics, focussing on those involving random matrices. 
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Topic: 
Machine Learning for Thermodynamic Observables in Lattice Field Theories 
Date: 
06.07.21 
Time: 
14:15 
Place: 
Online, via ZOOM 
Guest: 

Perimeter Institute, Ontario, Canada 

Abstract: 
In this talk, I will discuss how applying machine learning techniques to lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic. More specifically, I will show that deep generative models can be used to estimate thermodynamic observables like the free energy, which contrasts with existing MCMCbased methods that are limited to only estimate free energy differences. I will demonstrate the effectiveness of the proposed method for twodimensional $\phi^4$ theory and compare it to MCMCbased methods in detailed numerical experiments. 
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Topic: 
14:30 Untersuchung von frustrierten Spin1/2Systemen mit Hilfe von quantumthreecoloring am Beispiel des Kuboktaeders 
Date: 
14.10.21 
Time: 
14:30 
Place: 
Hybrid  Zoom/D5153 
Guest: 
Florian Brökemeier 
Universität Bielefeld 

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Topic: 
On NonHermitian BetaEnsembles 
Date: 
14.10.21 
Time: 
16:00 
Place: 
D5153 
Guest: 

Universität Bielefeld 

Abstract: 
Loggases with inverse temperature beta are systems with many applications in physics, for example in the theory of superconductors or the fractional quantum Hall effect. For some specific values of beta a correspondence to random matrix theory (RMT) is well established. The advantage of this connection is the usage of the RMT methods in the study of those systems. The goal of this talk is the discussion of Loggases in two dimensions, i.e. in the nonHermitian case, for more general values of the inverse temperature. Therefore, we study in the first part a model of normal 2 × 2 matrices with beta in [0,2] and discuss whether we find a surmise for the nearestneighbour spacing distribution of large matrices. In the second part of the talk we introduce the study of symmetry classes in nonHermitian RMT. We conjecture that the classes of complex symmetric and complex quaternion matrices can be effectively described by Loggases in two dimensions with noninteger inverse temperatures. 
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Topic: 
Central Limit Theorems to Stable and Invariant Random Matrices 
Date: 
20.10.21 
Time: 
09:00 
Place: 
ZOOM / Konferenzschaltung 
Guest: 

Melbourne University 

Abstract: 
Heavytailed random matrices have surprising and novel effects that can be hardly seen with the classical ensembles. For instance, in recent years it was shown that heavytailed Wigner matrices can exhibit localised eigenvector statistics for the eigenvalues in the tail while everything stays the same as we know it for the bulk statistics of a GUE. This effect, some intriguing as well as real world applications, and some own numerical experiments have motivated us to study invariant heavytailed random matrices. One of the questions we have addressed has been about central limit theorems at fixed matrix dimensions and invariant random matrices that are stable when adding independent copies of the random matrix under consideration. I will report on our new findings and will sketch the main ideas of their proofs in the present talk. These projects have been carried out in collaboration with Jiyuan Zhang and Adam Monteleone. 
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