gemseo / problems / scalable / parametric / core

design_space module

Scalable problem - Design space

Classes:

TMDesignSpace([n_shared, n_local, …])

The design space for the scalable problem introduced by Tedford and Martins (2010) defines the lower and upper bounds of both local design parameters, shared design parameters and coupling variables, as well as default values.
class gemseo.problems.scalable.parametric.core.design_space.TMDesignSpace(n_shared=1, n_local=None, n_coupling=None, default_inputs=None, dtype='float64')[source]

Bases: object

The design space for the scalable problem introduced by Tedford and Martins (2010) defines the lower and upper bounds of both local design parameters, shared design parameters and coupling variables, as well as default values.

The lower bounds are all equal to 0, the upper bounds are all equal to 1 and default values are all equal to 0.5.

The construction of the design space requires the number of shared design parameters, the number of local design parameters per discipline and the number of coupling variables per discipline. The two latter arguments must be list of integers with the same length which corresponds to the number of strongly coupled disciplines. By default, the design space considers two disciplines.

Parameters

  • n_shared (int) – size of the shared design parameters. Default: 1.

  • n_local (list(int)) – sizes of the local design parameters. If None, use [1, 1]. Default: None.

  • n_coupling (list(int)) – sizes of the coupling parameters. If None, use [1, 1]. Default: None.

  • default_inputs (dict) – default inputs. Default: None.

  • dtype – numpy data type. Default: ‘float64’.