Bayesian Optimization and Design of Experiments
Design-of-experiment (DOE) generator for science, engineering, and statistics
Design of Experiment Generator. Read the docs at: https://doepy.readthedocs.io/en/latest/
Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.
Framework for Data-Driven Design & Analysis of Structures & Materials (F3DASM)
Experimental design and Bayesian optimization library in Python/PyTorch
Design of Experiments in Julia
Curated list of resources for the Design of Experiments (DOE)
BASM - 2017 Spring
Flexible and accessible design of experiments in Python. Provides industry with an easy package to create designs based with limited expert knowledge. Provides researchers with the ability to easily create new criteria and design structures.
python experiment management toolset
Python library for Design and Analysis of Experiments
Python package for flexible generation of D-optimal experimental designs
Design of Experiments and Analysis
A tool for remote experiment management
Blocking and randomization for experimental design
Simulation and Analysis Tool for TAP Reactor Systems
Open-source constructor of surrogates and metamodels
Accelerate 2024 Workshop on Bayesian Optimization Recipes With BayBE
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