rsmine
is a Python package, implemented using Tensorflow, for optimising coarse-graining rules for real-space renormalisation group by maximising real-space mutual information.
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)].
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
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
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
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.
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}
}