Transport solver for a two-terminal superconducting junction for probing a tight-binding mean-field structure
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Updated
Mar 11, 2024 - C++
Transport solver for a two-terminal superconducting junction for probing a tight-binding mean-field structure
Numerical integration of mean-field equations for large-scale leaky integrate-and-fire neuronal network simulations incorporating synaptic plasticity via Graupner Brunel model. Includes support for a memory-induction stim-pop.
Factorized variational approximation using a univariate Gaussian distribution over a single variable x.
A project to study Hartree-Fock technique in 1-D
Model Reduction of the Approximate Master Equation for Epidemic Processes on Complex Networks
Path integral based Auxiliary field Monte-Carlo results for Fermionic Hubbard model using HF decoupling
3 states magnetic model
A bi-virus epidemic model for networks with duty-cycled wireless sensors
Supplements the paper “Self-Aware Transport of Economic Agents” [SATHA] by Andrew Lyasoff
description coming soon
A C++ program for solving the mean field equation in Holstein model and periodic Anderson model with Holstein phonons, with phonon displacement as the order parameter.
Implementation of Variational Mean Field Inference for dense Conditional Random Fields.
This repo provide numerical result of Caroli-de Gennes-Matricon(CdGM) mode and pair potential of an isolated quantum vortex in the two-dimensional superconductor .
Hartree Fock corrections to nearly free electron Bloch bands
A collection of programs and scripts to solve and analyze the Kane-Mele-Hubbard model in a variety of (dynamical) mean-field settings
Projects of the Statistical Learning Theory class at ETH Zurich
Mean field variational Gaussian process algorithm. This repository contains a python3 implementation of the variational mean field algorithm as described in the paper: Physical Review E. vol. 91, 2015, 012148.
Here I implemented a simulation to predict the outcome of the FIFA Men's World Cup 2022.
Mean field theory and cavity method implementation.
Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.
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