Advanced topics

The following chapter introduces advanced features and describes how to use the toolbox for solving real-world problems.

Degree of dimensional adaptivity   Balancing greedy vs. conservative refinement for dimension-adaptive interpolants

Multiple output variables   Interpolating objective models with multiple output variables

Derivatives Computing the gradient vector of the sparse grid interpolant at arbitrary points

Integration Numerical integration using sparse grids

Optimization   Discusses the search methods available to efficiently perform an optimization of the sparse grid interpolant

Improving performance   Provides an overview on how to achieve maximum performance of the Sparse Grid Interpolation Toolbox

Interfacing concepts Concepts of how to best interface existing Matlab models with spvals for the interpolant construction

Approximating ODEs Recovering ODEs with one or several time-dependent outputs for a range of initial conditions or parameters

External models Interfacing spvals with models not implemented in Matlab

Degree of dimensional adaptivity	Balancing greedy vs. conservative refinement for dimension-adaptive interpolants
Multiple output variables	Interpolating objective models with multiple output variables
Derivatives	Computing the gradient vector of the sparse grid interpolant at arbitrary points
Integration	Numerical integration using sparse grids
Optimization	Discusses the search methods available to efficiently perform an optimization of the sparse grid interpolant
Improving performance	Provides an overview on how to achieve maximum performance of the Sparse Grid Interpolation Toolbox
Interfacing concepts	Concepts of how to best interface existing Matlab models with `spvals` for the interpolant construction
Approximating ODEs	Recovering ODEs with one or several time-dependent outputs for a range of initial conditions or parameters
External models	Interfacing `spvals` with models not implemented in Matlab