Papers › Perturbative dynamic renormalization of scalar field theories in statistical physics
Perturbative dynamic renormalization of scalar field theories in statistical physics
Nikos Papanikolaou, Thomas Speck
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Renormalization is a powerful technique in statistical physics to extract the large-scale behavior of interacting many-body models. These notes aim to give an introduction to perturbative methods that operate on the level of the stochastic evolution equation for a scalar field (e.g., density), including systems that are driven away from equilibrium and thus lack a free energy. While there is a large number of reviews and lecture notes, many are somewhat scarce on technical details and written in the language of quantum field theory, which can be more confusing than helpful. Here we attempt a minimal and concise yet pedagogical introduction to dynamic renormalization in the language of statistical physics with a strong focus on how to actually perform calculations. We provide a symbolic algebra implementation of the discussed techniques including Jupyter notebooks of two illustrations: the KPZ equation and a neural network model.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections