emdgroup/baybe-ac24-workshop
Accelerate 2024 Workshop on Bayesian Optimization Recipes With BayBE
This project helps scientists and engineers running real-world experiments to optimize their processes more effectively. It takes experimental data, including categorical choices like chemical compounds or specific mixtures, and provides recommendations for the next best experimental steps. This is designed for researchers, chemists, and material scientists who need to refine experimental parameters to achieve desired outcomes.
No commits in the last 6 months.
Use this if you are conducting experiments where you need to systematically find optimal conditions, especially when dealing with categorical variables, complex mixtures, or when you have historical data from similar experiments.
Not ideal if your experiments involve very few variables, or if you are looking for a simple, non-AI-driven approach to parameter tuning.
Stars
10
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Aug 06, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/emdgroup/baybe-ac24-workshop"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SimonBlanke/Gradient-Free-Optimizers
Lightweight optimization with local, global, population-based and sequential techniques across...
Gurobi/gurobi-machinelearning
Formulate trained predictors in Gurobi models
emdgroup/baybe
Bayesian Optimization and Design of Experiments
heal-research/pyoperon
Python bindings and scikit-learn interface for the Operon library for symbolic regression.
simon-hirsch/ondil
A package for online distributional learning.