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
BASM - 2017 Spring
Curated list of resources for the Design of Experiments (DOE)
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
Simulation and Analysis Tool for TAP Reactor Systems
Open-source constructor of surrogates and metamodels
Blocking and randomization for experimental design
Accelerate 2024 Workshop on Bayesian Optimization Recipes With BayBE
Autonomously driving equation discovery, from the micro to the macro, from laptops to supercomputers.
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