Note
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Variables influence#
In this example, we illustrate the use of the VariableInfluence plot
on the Sobieski's SSBJ problem.
The VariableInfluence post-processing performs first-order variable
influence analysis.
The method computes \(\frac{d f}{d x_i} \cdot \left(x_{i_*} - x_{initial_design}\right)\), where \(x_{initial_design}\) is the initial value of the variable and \(x_{i_*}\) is the optimal value of the variable.
![9 variables explain 99% of -y_4, 5 variables explain 99% of g_1[0], 5 variables explain 99% of g_1[1], 5 variables explain 99% of g_1[2], 5 variables explain 99% of g_1[3], 5 variables explain 99% of g_1[4], 4 variables explain 99% of g_1[5], 4 variables explain 99% of g_1[6], 1 variables explain 99% of g_2, 7 variables explain 99% of g_3[0], 7 variables explain 99% of g_3[1], 3 variables explain 99% of g_3[2], 3 variables explain 99% of g_3[3]](../../../_images/sphx_glr_plot_variable_influence_001.png)
<gemseo.post.variable_influence.VariableInfluence object at 0x7f522c31c350>
from __future__ import annotations
from gemseo import execute_post
from gemseo.settings.post import VariableInfluence_Settings
execute_post(
"sobieski_mdf_scenario.h5",
settings_model=VariableInfluence_Settings(save=False, show=True),
)
Total running time of the script: (0 minutes 1.283 seconds)