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
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Updated
Aug 22, 2022 - Python
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
Nuclear physics at low renormalization group resolution.
Strong Disorder RG Flow and the Random Singlet Phase of a 1D Random AF Heisenberg Spin-1/2 Chain.
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.
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
Ising model, Glauber dynamics, Metropolis-Hastings algorithms, and renormalization.
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.
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
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.
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