This master’s level course, Methods of Stochastic Simulations in Statistical Physics: Interdisciplinary Applications, provides a comprehensive foundation in the theory and application of Monte Carlo techniques to complex physical and interdisciplinary systems, specifically designed for MSc students from Computational Physics and from Phenomenology and Experimental High-Energy Physics program. Building upon advanced proficiency in Python and C++, the curriculum bridges the gap between deterministic dynamics and stochastic modeling, covering essential topics such as Markov processes, Metropolis and Glauber dynamics, the BKL (Kinetic MC) algorithm, and cluster updates (Swendsen-Wang and Wolff) to overcome critical slowing down. Students will explore the transition from fundamental statistical mechanics—including ensemble theory and phase transitions—to modern research-level applications in materials science (surface deposition, grain growth), soft matter, and computational optimization (simulated annealing), equipping them with the high-performance numerical tools necessary for tackling non-deterministic problems in contemporary science.
- Teacher: Ferenc Járai-Szabó
- Teacher: Botond Tyukodi