Numerical exploration of statistical physics

Bukva, A. (2023)

Doctoral Thesis

In this thesis, we examine various systems through the lens of several numerical methods. We delve into questions concerning thermalization in closed unitary systems, lattice gauge theories, and the intriguing properties of deep neural network phase spaces. Leveraging modern advancements in both software and hardware, we scrutinize these systems in greater detail, accessing previously unreachable regimes.

• Numerical methods
• Statistical physics
• Lattice gauge theory
• Machine learning
• Monte Carlo methods
• Thermalization

• All authors: Bukva, A.
• Supervisor: Zaanen, J.; Schalm, K.E.
• Committee: Beenakker, C.W.J.; Tura Brugués, J.; Wezel, J. van; Achúcarro, A.; Caux, J.S.
• Qualification: Doctor (dr.)
• Awarding Institution: Leiden Institute of Physics (LION), Faculty of Science, Leiden University
• Date: 2023-10-10
• Title of host publication: Casimir PhD Series
• ISBN (print): 9789085935711
• Name: 2023-23