EmuKit/emukit
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
This toolkit helps scientists, engineers, and researchers make better decisions when data is hard to get or experiments are expensive. You can input limited experimental observations or simulation results and get out optimized experiment designs, tuned model parameters, or deeper insights into how your system's inputs affect its outputs. It's for anyone dealing with complex systems where resources for data collection are constrained.
647 stars. Used by 2 other packages. Available on PyPI.
Use this if you need to optimize physical experiments, tune complex algorithm parameters, or understand system sensitivities with limited data.
Not ideal if you have abundant data, low-cost experiments, or need a pre-built, end-to-end solution for a very specific problem domain.
Stars
647
Forks
132
Language
Python
License
Apache-2.0
Last pushed
Feb 22, 2026
Commits (30d)
0
Dependencies
4
Reverse dependents
2
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/EmuKit/emukit"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
google/uncertainty-baselines
High-quality implementations of standard and SOTA methods on a variety of tasks.
nielstron/quantulum3
Library for unit extraction - fork of quantulum for python3
IBM/UQ360
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you...
aamini/evidential-deep-learning
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
nikitadurasov/masksembles
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).