Scan variable
This variable scans the output of a surrogate model over design or environment parameters. A typical use case is to determine the system behavior based on an interpolation between data points for different environment parameter values. An interpolation can reduce the cost of obtaining observations at small steps between environment parameters.
name (str)
The name of the variable under which it can be addressed by other variables or objectives. The name must be distinct from any surrogate name.
Default:
"v"
output_dim (int)
Output dimension of variable.
Default: This value has no default and must be provided
output_names (cell{str})
Allows to assign names to each of the outputs of the variable. By specifying input names they can be accessed in variables as
{output_names[0]}, {output_names[1]}, ..., {output_names[K-1]}, whereKis the output dimension.Default:
By default, the variables can be accessed as
{name}0, {name}1, ...,{name}(K-1), where{name}is the name of the variable andKis the output dimension.
input_surrogate (str)
The name of a surrogate.
Default: This value has no default and must be provided
scan_parameters (cell{str})
The names of all parameters \(p_1, p_2,\dots, p_M\) whose values is scanned. Each name must match a design or environment parameter.
Default: This value has no default and must be provided
scan_values (cell{cell{float}})
The values of the scan parameters. This is a list of \(N\) vectors where each entry is a list of \(M\) values for each of the scan parameters. If the input has multiple outputs \(y_1,\dots,y_K\), the outputs of the scan variable are sorted as \(y_{11}, y_{12}, \dots, y_{1M}, y_{21}, y_{22}, y_{2M},\dots y_{K1}, y_{K2}, \dots, y_{KM}\).
Default: This value has no default and must be provided
Example
A list with of six scan values for two parameters.
{{1.0,0.0},... {1.0,0.2},... {1.0,0.4},... {2.0,0.0},... {2.0,0.2},... {2.0,0.4}}
correlate_outputs (bool)
If true, a covariance matrix between all scan outputs is determined and used for the predictions. In cases, where some outputs are linearly dependent, this approach fails since the covariance matrix becomes ill-conditioned. The option takes only effect if the input surrogate is a Gaussian process with no warping applied.
Default:
false