Bayesian Optimization and Design of Experiments
Design of experiments (DoE) and machine learning packages for the iCFree project
Organized various ML models together
Framework for Data-Driven Design & Analysis of Structures & Materials (F3DASM)
A tool for remote experiment management
Active learning tool for designing experiments
Experimental design and Bayesian optimization library in Python/PyTorch
ANOVA-Simultaneous Component Analysis
Design of Experiment Generator. Read the docs at: https://doepy.readthedocs.io/en/latest/
Providing ability to feed multiple parameters to webpage https://virtualtrebuchet.com/ for Design and Experiment Optimization in JMP
Autonomously driving equation discovery, from the micro to the macro, from laptops to supercomputers.
Simulation and Analysis Tool for TAP Reactor Systems
Generate and characterize designs with four-and-two-level (FATL) factors.
Python package for flexible generation of D-optimal experimental designs
Object-Orientated Derivative-Free Optimisation
Simple implementation of Latin Hypercube Sampling.
A tool for the analysis of datasets obtained by performing experiments according to a Design of Experiments (DoE) approach.
This code will run the analysis for a 2k Factorial design of experiments test with 1 to 26 variable parameters of interest.
Catalog of all regular fractional factorial designs from Chen, Sun and Wu (1993) and Xu (2009)
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