rsmine 0.3.0a1

Optimal coarse graining transformations with RSMI neural estimation

RSMI-NE

rsmine 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
• Citation

Overview

rsmine 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 manuscripts accompanying this code [D.E. Gökmen, Z. Ringel, S.D. Huber and M. Koch-Janusz, Phys. Rev. Lett. 127, 240603 (2021) and Phys. Rev. E 104, 064106 (2021)].

System requirements

Hardware requirements

rsmine 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)
  • Monterey (12.5.1)

rsmine mainly depends on the following Python packages:

• matplotlib
• numpy
• pandas
• scipy
• scikit-learn
• tensorflow 2.0
• tensorflow-probability

Installation

Clone RSMI-NE from GitHub

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

and install the rsmine package via pip in editable mode

pip install -e .

or create a virtual environment and install there:

./install.sh

Getting started

The package can be used by importing the rsmine module and its submodules:

import rsmine

import rsmine.coarsegrainer.build_dataset as ds
import rsmine.coarsegrainer.cg_optimisers as cg_opt

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

Citation

If you use RSMI-NE in your work, please cite our publications Phys. Rev. Lett. 127, 240603 (2021) and Phys. Rev. E 104, 064106 (2021):

@article{PhysRevLett.127.240603,
  title = {Statistical Physics through the Lens of Real-Space Mutual Information},
  author = {G\"okmen, Doruk Efe and Ringel, Zohar and Huber, Sebastian D. and Koch-Janusz, Maciej},
  journal = {Phys. Rev. Lett.},
  volume = {127},
  issue = {24},
  pages = {240603},
  numpages = {7},
  year = {2021},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.127.240603},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.127.240603}
}

@article{PhysRevE.104.064106,
  title = {Symmetries and phase diagrams with real-space mutual information neural estimation},
  author = {G\"okmen, Doruk Efe and Ringel, Zohar and Huber, Sebastian D. and Koch-Janusz, Maciej},
  journal = {Phys. Rev. E},
  volume = {104},
  issue = {6},
  pages = {064106},
  numpages = {17},
  year = {2021},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.104.064106},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.104.064106}
}