gemseo / problems / sobieski / core

Show inherited members

design_space module

The design space of the Sobieski’s SSBJ problem.

class gemseo.problems.sobieski.core.design_space.SobieskiDesignSpace(use_original_names=True, dtype=DataType.FLOAT, use_original_design_variables_order=False)[source]

Bases: DesignSpace

The design space of the Sobieski’s SSBJ problem.

Note

This design space includes both the design and coupling variables.

Parameters:
  • use_original_names (bool) –

    Whether to use physical naming instead of original notations.

    By default it is set to True.

  • dtype (SobieskiBase.DataType) –

    The data type for the NumPy arrays, either “float64” or “complex128”.

    By default it is set to “float64”.

  • use_original_design_variables_order (bool) –

    Whether to sort the DesignSpace as in [SSAJr98]. If so, the order of the design variables will be "x_1", "x_2", "x_3" and "x_shared". Otherwise, "x_shared", "x_1", "x_2" and "x_3".

    By default it is set to False.

filter_coupling_variables(copy=False)[source]

Filter the design space to keep only the coupling variables.

Parameters:

copy (bool) –

Whether to filter a copy of the design space or the design space itself.

By default it is set to False.

Returns:

Either the filtered original design space or a copy.

Return type:

SobieskiDesignSpace

filter_design_variables(copy=False)[source]

Filter the design space to keep only the design variables.

Parameters:

copy (bool) –

Whether to filter a copy of the design space or the design space itself.

By default it is set to False.

Returns:

Either the filtered original design space or a copy.

Return type:

SobieskiDesignSpace

dimension: int

The total dimension of the space, corresponding to the sum of the sizes of the variables.

name: str | None

The name of the space.

normalize: dict[str, ndarray]

The normalization policies of the variables components indexed by the variables names; if True, the component can be normalized.

variable_names: list[str]

The names of the variables.

variable_sizes: dict[str, int]

The sizes of the variables.

variable_types: dict[str, ndarray]

The types of the variables components, which can be any DesignSpace.DesignVariableType.

Examples using SobieskiDesignSpace

Example for exterior penalty applied to the Sobieski test case.

Example for exterior penalty applied to the Sobieski test case.

Empirical estimation of statistics

Empirical estimation of statistics

Gantt Chart

Gantt Chart

Application: Sobieski’s Super-Sonic Business Jet (MDO)

Application: Sobieski's Super-Sonic Business Jet (MDO)

Scalable diagonal discipline

Scalable diagonal discipline

BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based DOE on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case

BiLevel-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case

IDF-based MDO on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case

MDF-based DOE on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

MDF-based MDO on the Sobieski SSBJ test case

Simple disciplinary DOE example on the Sobieski SSBJ test case

Simple disciplinary DOE example on the Sobieski SSBJ test case

Plug a surrogate discipline in a Scenario

Plug a surrogate discipline in a Scenario

Basic history

Basic history

Constraints history

Constraints history

Correlations

Correlations

Gradient Sensitivity

Gradient Sensitivity

Objective and constraints history

Objective and constraints history

Optimization History View

Optimization History View

Parallel coordinates

Parallel coordinates

Pareto front

Pareto front

Quadratic approximations

Quadratic approximations

Radar chart

Radar chart

Robustness

Robustness

Scatter plot matrix

Scatter plot matrix

Self-Organizing Map

Self-Organizing Map

Variables influence

Variables influence