README.md

SMT Toolbox

These tutorials introduce to use the opensource Surrogate Modeling Toolbox where different surrogate models are available.

SMT Tutorial (linear, quadratic, gaussian process, ...)

Surrogate-based Optimization

• Efficient Global Optimization: How to start?

• Bayesian Optimization - Efficient Global Optimization to solve expensive problems

• Bayesian Optimization with noisy data

Multi-Fidelity Gaussian Process

• With required nested sampling

• With noise

• Adaptative sampling

• Without nested sampling

Proper Orthogonal Decomposition and Interpolation

• PODI+I tutorial in SMT with global and local basis

• PODI+I application to airfoil design

Kernel Engineering

• Kernel engineering tutorial in SMT

• Kernel engineering application to aeroelasticity prediction

Explainability and conformal prediction

• Warning: The explainability usage tutorial has been moved to SMTorg/smt-explainability

Other Gaussian Process Models and Sampling Methods

• LHS sampling (initial and expanded)

• Gaussian Process Trajectory Sampling

• Noisy Gaussian Process

• Sparse Gaussian Process

• Cooperative Components Kriging

Mixed-integer and mixed-hierarchical surrogate models

• Warning: The Design Space usage tutorial has been moved to SMTorg/smt-design-space-ext

• Specific notebook associated to the SMT 2.0 Journal Paper (submitted) with a focus on mixed integer and mixed hierarchical surrogate models (continuous, discrete, categorical)

• Mixed-Integer Gaussian Process and Bayesian Optimization to solve unconstrained problems with mixed variables (continuous, discrete, categorical)

• Mixed-Integer Gaussian Process and Bayesian Optimization for Engineering application