This is project page for the paper "RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior". Paper link: https://arxiv.org/abs/2010.00029

Pytorch source code for arXiv paper Neural Network Renormalization Group, a generative model using variational renormalization group and normalizing flow.

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

Implementation of the GILT + HOTRG and calculating scaling dimensions through the linearized RG equation of the GILT + HOTRG.

PyR@TE 3

Official implementation of spectrum bifurcation renormalization group(SBRG), which is suitable for quantum simulation on strong disordered systems for 1D and 2D. Paper: arXiv:2008.02285[https://arxiv.org/abs/2008.02285], Phys. Rev. B 93, 104205 (2016)[https://arxiv.org/abs/1508.03635]

A Tensor Network package for Machine Learning and Quantum Computing in Python.

Using simple TRG code to benchmark different tensor implements.

Renormalization for the break-up of invariant tori in Hamiltonian flows

Code for RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior.

Floquet real-time renormalization group implemention in python for the single channel Kondo model

Pipeline Consisting of LSTM + Variational and Transformer Based Autoencoders + PCA/UMAP (Parameterized and Non-Parameterized) For Generating Low-Dim Manifold Representation of V1 Neural Activity