grad_W_kappa_ij2

Computes gradient of Int(kappa_i(X, design1) . kappa_j(design2, X)) with respect to the first argument.

internal

The active subspace method is a sensitivity analysis technique that finds important linear combinations of input variables for a simulator. This package provides functions allowing estimation of the active subspace without gradient information using Gaussian processes as well as sequential experimental design tools to minimize the amount of data required to do so. Implements Wycoff et al. (JCGS, 2021) <doi:10.48550/arXiv.1907.11572>.

Nathan Wycoff

activegp

Gaussian Process Based Design and Analysis for the Active
Subspace Method

grad_W_kappa_ij2 function

<dl><dt>design1</dt>
<dd>A vector representing a new point.</dd>
<dt>design2</dt>
<dd>matrices of design points</dd>
<dt>theta</dt>
<dd>lengthscales</dd>
<dt>i1, i2</dt>
<dd>index of the derivatives (WARNING: starts at 0)</dd>
<dt>ct</dt>
<dd>Covariance type, 1 means Gaussian, 2 means Matern 3/2, 3 means Matern 5/2</dd></dl>

Arguments

Covariance of kernel computations — grad_W_kappa_ij2

<dl>

<dt>design1</dt>
<dd>A vector representing a new point.</dd>


<dt>design2</dt>
<dd>matrices of design points</dd>


<dt>theta</dt>
<dd>lengthscales</dd>


<dt>i1, i2</dt>
<dd>index of the derivatives (WARNING: starts at 0)</dd>


<dt>ct</dt>
<dd>Covariance type, 1 means Gaussian, 2 means Matern 3/2, 3 means Matern 5/2</dd>

</dl>

grad_W_kappa_ij2: Covariance of kernel computations

Description

Usage

Value

Arguments