funGp-package: Gaussian Process Models for Scalar and Functional Inputs

In funGp: Gaussian Process Models for Scalar and Functional Inputs

Gaussian Process Models for Scalar and Functional Inputs

Description

Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. Smart selection is based on Ant Colony Optimization ACO algorithm.

Base functionalities

• Main methods
  fgpm: creation of funGp regression models
  predict,fgpm-method: output estimation at new input points based on a funGp model
  simulate,fgpm-method: random sampling from a funGp Gaussian process model
  update,fgpm-method: modification of data and hyperparameters of a funGp model

• Plotters
  plot,fgpm-method: validation plot for a fgpm model
  plot.predict.fgpm: plot of predictions based on a fgpm model
  plot.simulate.fgpm: plot of simulations based on a fgpm model

Model selection

• Main method
  fgpm_factory: structural parameter optimization

• Functions for pre-optimization
  decay: regularized initial pheromones
  decay2probs: normalized initial pheromones

• Plotters post-optimization
  plot,Xfgpm-method: plot of the evolution of the algorithm with which = "evolution" or of the absolute and relative quality of the optimized model with which = "diag"

• Correction post-optimization of input data structures
  which_on: indices of active inputs in a model structure delivered by fgpm_factory
  get_active_in: extraction of active input data based on a model structure delivered by fgpm_factory

Useful material

• Manual Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour

• Paper Gaussian process metamodeling of functional-input code for coastal flood hazard assessment

• Tech. report Ant Colony Based Model Selection for Functional-Input Gaussian Process Regression

Authors

José Betancourt, François Bachoc and Thierry Klein

Contributors

Déborah Idier and Jérémy Rohmer

Note

This package was first developed in the frame of the RISCOPE research project, funded by the French Agence Nationale de la Recherche (ANR) for the period 2017-2021 (ANR, project No. 16CE04-0011, RISCOPE.fr), and certified by SAFE Cluster.

funGp documentation built on April 25, 2023, 9:07 a.m.