SciML/Surrogates.jl

SMT: The Surrogate Modeling Toolbox

Stochastic Optimization, Learning, Uncertainty and Sampling

Derivative-free parameter calibration and uncertainty quantification for expensive models using ensemble Kalman methods

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Black-box optimization for Julia

Emulate simulations easily

Hyperparameter optimization in Julia.

Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods

A Python implementation of global optimization with gaussian processes.