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

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.

Implementation of Zipper Entanglement Renormalization on Julia platform.

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

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

Constructing coarse grained super-dimers on the Ammann-Beenker quasicrystal with RSMI-NE.

This is the MS Project for partial fulfillment of the award of degree of MSc at IISER-K. The project involves solving the single-impurity Anderson model using a unitary renormalization group approach.

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