Welcome to mfpml’s documentation!

Links: Documentation | GitHub repository

First publication: August 23, 2023

Overview:

• Summary
  • Basic Methods
  • Advanced methods
  • Authorship
  • Community Support
  • License

Get started:

• Installation

Design of experiments:

• Single-Fidelity Samplers
• Multi-Fidelity Samplers
• Summary

Problems:

• Problems class structure
• Single fidelity problems
  • Example for single fidelity function
  • Implemented single fidelity Functions
• Multi fidelity problems
  • Example for multi fidelity function
  • Implemented multi fidelity Functions

Surrogates:

• Single fidelity surrogates
  • Kriging model
    • 1. basics of Kriging model
    • 2. Negative log-likelihood
    • 3. Inference
    • 4. Implementation
  • Gaussian process regression for noisy data
    • 1. basics of general Gaussian process regression
    • 1. Implementation
• Multi fidelity surrogates
  • Co-Kriging model
    • 1. basics of Co-Kriging model
    • 2. Training and Inference
  • Implementation
  • Implemented multi-fidelity surrogates
  • References

Optimization:

• Evolutionary algorithms
  • Particle swarm optimization
  • Differential evolution
• Single-fidelity Bayesian optimization
  • 2.1 acquisition functions
  • 2.2 Schematics of Acquisition function on Forrester function
  • Implemented single fidelity acquisition functions
• Multi-fidelity Bayesian optimization
  • Implementation
  • Implemented multi-fidelity acquisition functions
  • References

API Reference:

• API Documentation
  • Modules
    • mfpml.design_of_experiment
    • mfpml.problems
    • mfpml.models
    • mfpml.optimization

Indices and tables

• Index

• Module Index

• Search Page