npumazehong / rsmi-ne

A Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.

RSMI-NE

RSMI-NE is a Python package, implemented using Tensorflow, for optimising coarse-graining rules for real-space renormalisation group by maximising real-space mutual information.

• Overview
• System requirements
• Installation
• License

Overview

RSMI-NE employs state-of-the-art results for estimating mutual information (MI) by maximising its lower-bounds parametrised by deep neural networks [Poole et al. (2019), arXiv:1905.06922v1]. This allows it to overcome the severe limitations of the initial proposals for constructing real-space RG transformations by MI-maximization in [M. Koch-Janusz and Z. Ringel, Nature Phys. 14, 578-582 (2018), P.M. Lenggenhager et al., Phys.Rev. X 10, 011037 (2020)], and to reconstruct the relevant operators of the theory, as detailed in the manuscript accompanying this code [D.E. Gökmen, Z. Ringel, S.D. Huber and M. Koch-Janusz, "Statistical physics through the lens of real-space mutual information"].

System requirements

Hardware requirements

RSMI-NE can be run on a standard personal computer. It has been tested on the following setup (without GPU):

• CPU: 2.3 GHz Quad-Core Intel Core i5, Memory: 8 GB 2133 MHz LPDDR3

Software requirements

This package has been tested on the following systems with Python 3.8.5:

• macOS:
  • Catalina (10.15)
  • Big Sur (11.1)

RSMI-NE mainly depends on the following Python packages:

• matplotlib
• numpy
• pandas
• scipy
• sklearn
• tensorflow 2.0
• tensorflow_probability

Installation

Clone RSMI-NE from Github and install its dependencies into a virtual environment:

git clone https://github.com/RSMI-NE/RSMI-NE
cd RSMI-NE
./install/install.sh

Getting started

Jupyter notebooks demonstrating the basic usage in simple examples are provided in https://github.com/RSMI-NE/RSMI-NE/tree/main/coarsegrainer/examples.

License

This project is covered under the Apache 2.0 License.