gemseo.formulations.disciplinary_opt module#

A formulation for uncoupled or weakly coupled problems.

class DisciplinaryOpt(disciplines, objective_name, design_space, settings_model=None, **settings)[source]#

Bases: BaseMDOFormulation

The disciplinary optimization.

This formulation draws the architecture of a mono-disciplinary optimization process from an ordered list of disciplines, an objective function and a design space.

Initialize self. See help(type(self)) for accurate signature.

Parameters:

  • disciplines (Sequence[Discipline]) -- The disciplines.

  • objective_name (str) -- The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.

  • design_space (DesignSpace) -- The design space.

  • settings_model (DisciplinaryOpt_Settings | None) -- The settings of the formulation as a Pydantic model. If None, use **settings.

  • **settings (Any) -- The settings of the formulation. This argument is ignored when settings_model is not None.

Settings#

alias of DisciplinaryOpt_Settings

get_top_level_disciplines()[source]#

Return the disciplines which inputs are required to run the scenario.

A formulation seeks to compute the objective and constraints from the input variables. It structures the optimization problem into multiple levels of disciplines. The disciplines directly depending on these inputs are called top level disciplines.

By default, this method returns all disciplines. This method can be overloaded by subclasses.

Returns:

The top level disciplines.

Return type:

tuple[Discipline]