Codebase release 0.7 for TNRKit.jl

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Authors: Victor Vanthilt, adwait naravane, Chenqi Meng, Atsushi Ueda
Cited by: 1
References: 1

Abstract

We present TNRKit.jl, an open-source Julia package for Tensor Network Renormalization (TNR) of two- and three-dimensional classical statistical models and Euclidean lattice field theories. Built on top of TensorKit.jl, it provides a symmetry-aware framework for constructing tensor-network representations of partition functions and coarse-graining them using methods such as TRG, HOTRG, and LoopTNR. Beyond thermodynamic quantities, the package enables the extraction of universal conformal data – including scaling dimensions and the central charge – directly from fixed-point tensors. TNRKit.jl is designed with both usability and extensibility in mind, offering a practical platform for applying, benchmarking, and developing modern tensor renormalization algorithms. This paper also serves as a self-contained introduction to the TNR framework.

Fields of Study & Subjects

Quantum many-body systems Tensor decomposition and applications Machine Learning in Materials Science

Publication details

Journal / source
SciPost Physics Codebases
Publisher
Stichting SciPost
Publication date
7 July 2026
Language
EN