Easy whole-brain modeling for computational neuroscientists 🧠💻👩🏿🔬
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Updated
May 13, 2024 - Python
Easy whole-brain modeling for computational neuroscientists 🧠💻👩🏿🔬
Python library to compute different properties of quantum tight binding models in a lattice
Package for solving generalized BdG mean field theory of interacting systems.
User-friendly open-source software to design and solve tight-binding models, addressing electronic properties, topology, interactions, non-collinear magnetism, and unconventional superconductivity, among others.
Transport solver for a two-terminal superconducting junction for probing a tight-binding mean-field structure
A collection of programs and scripts to solve and analyze the Kane-Mele-Hubbard model in a variety of (dynamical) mean-field settings
Supplements the paper “Self-Aware Transport of Economic Agents” [SATHA] by Andrew Lyasoff
Physics-inspired transformer modules based on mean-field dynamics of vector-spin models in JAX
Computational statistical mechanics of field-responsive polymer chains
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 .
A bi-virus epidemic model for networks with duty-cycled wireless sensors
Mean field methods in the context of continuum mechanics with special focus on orientation averaging homogenization
Here I implemented a simulation to predict the outcome of the FIFA Men's World Cup 2022.
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.
Model Reduction of the Approximate Master Equation for Epidemic Processes on Complex Networks
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.
Factorized variational approximation using a univariate Gaussian distribution over a single variable x.
Python library to compute different properties of tight binding models
Projects of the Statistical Learning Theory class at ETH Zurich
Package to perform tight binding calculation in tight binding models, with a friendly user interface
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