UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

This is an implementation of Deutsch and Deutsch, "Latin hypercube sampling with multidimensional uniformity", Journal of Statistical Planning and Inference 142 (2012) , 763-772

Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods

Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples

This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.

Latin hypercube sampling code for the lhs pacakge in the statistical software package R (www.r-project.org)

Simple implementation of Latin Hypercube Sampling.

ChemDesign: DWSIM Experiment Toolkit

Design of experiments (DoE) and machine learning packages for the iCFree project

Wrapper for sampling methods in R and approximate star discrepancy calculation

Implementation, analysis and benchmarking of optimization algorithms. Developed in Python and results showed in Jupyter Notebook

Sampling and resampling techniques for random sample generation, estimation, and simulation

Hyperparameter tuning using a robust simulation optimization framework

Optimization using MPI parallel Latin hypercube sampling and BOBYQA