TY  - GEN
KW  - Neuromorphes Rechnen
KW  -  Deep Learning
ID  - heidok30802
CY  - Heidelberg
AV  - public
Y1  - 2021///
TI  - Deep Learning and Neuromorphic Computing in Quantum Chromodynamics and Beyond
N2  - Accompanied by the fast evolution of graphical processing units, there is a rapid development of deep learning methods with applications in almost all natural and applied sciences. Simultaneously, a growing interest is emerging around alternative, more energy- and time-efficient computing devices. Driven by these developments, we propose in this thesis several possible directions in the field of quantum chromodynamics and beyond. We start with the exploration of novel frontiers to perform scientific computing tasks on the spike-based BrainScaleS neuromorphic device. This includes numerical computations in statistical physics and the representation of entangled quantum states. We continue by establishing a new mathematical framework to tackle the so-called sign problem impeding a statistical analysis of many physical systems, including quantum chromodynamics at finite density. Dealing with the same problem, a machine learning-driven algorithm is proposed in the subsequent part of the thesis. Utilizing deep neural networks to recognize undiscovered structures and to get novel insights into physical data is a further direction pursued by employing methods from explainable machine learning and by proposing a new unsupervised training algorithm for generating lower-dimensional representations. The thesis concludes with a supervised learning framework for approaching the inverse problem of reconstructing spectral functions from Euclidean propagators.
A1  - Kades, Lukas
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/30802/
ER  -