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.

Novel real space renormalization group approach for many-body localization criticality problems

Using simple TRG code to benchmark different tensor implements.

Nuclear physics at low renormalization group resolution.

Notebooks developed in Mathematica for my Ph.D. thesis and other resources

TeNeT is a tensor-network renormalization code for the statistical mechanics of lattice models. The main application is adsorption of molecules on surfaces.

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

This repository implements a high-performance Inverse Renormalization Group (IRG) framework using Convolutional Neural Networks (CNN).

Royal Gramma UI / Renormalization Group UI

Code for Important Processing Steps For .Tiff or .Raw Video Files Acquired From 2-Photon Imaging System + Completely Automated and With NWB Schema (.HDF5) Capability

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

Hierarchical Tensor Networks approach: example code for diamond and folded square hierarchical lattices

Representation Group Flow — MSc Mathematics thesis (NISER, 2023): an effective statistical-field-theory of a deep MLP at initialization; how representations flow with depth.

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