Michalis Vrettas, PhD's Projects
Analysis code for the paper Man et. al. 2020.
Hamiltonian Monte Carlo (HMC) sampling method in Python3, based on the original paper: Simon Duane, Anthony D. Kennedy, Brian J. Pendleton and Duncan Roweth (1987). "Hybrid Monte Carlo". Physics Letters B. 195 (2): 216–222.
Hamiltonian Monte Carlo (HMC) sampling method in C++11.
Hydrological Model (Berkeley). This project implements the underground (stochastic) hydrological model (in Python) that was developed during my postdoctoral tenure at the Dept. of Earth & Planetary Science, U. C. Berkeley, (2013 - 2016).
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.
A machine learning-based approach for classifying metal site geometry.
This repository provides a Python implementation of the NapShift program to predict the backbone atoms' chemical shift values, from NMR protein PBD structures, using artificial neural networks.
Provides a Python implementation of the camcoil program (originally written in C) to estimate the random coil chemical shift values from a sequence (string) of amino-acids.
Genetic Algorithms in Python3. StandardGA and IslandModelGA (runs in parallel).
Scaled conjugate gradient (SCG) optimization algorithm.
Self organizing maps (SOM) in Python3.
Variational Gaussian Process Approximation. This project contains a python3 implementation of the original VGPA algorithm for approximate inference in SDEs.
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