Source code for gemseo.formulations.mdf

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
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# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# Contributors:
#    INITIAL AUTHORS - API and implementation and/or documentation
#        :author: Francois Gallard
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""
The Multi-disciplinary Design Feasible formulation
**************************************************
"""
from __future__ import absolute_import, division, unicode_literals

from future import standard_library

from gemseo.core.formulation import MDOFormulation
from gemseo.mda.mda_factory import MDAFactory

standard_library.install_aliases()


[docs]class MDF(MDOFormulation): """ The Multidisciplinary Design Feasible formulation draws an optimization architecture where the coupling of strongly coupled disciplines is made consistent by means of a Multidisciplinary Design Analysis (MDA), the optimization problem w.r.t. local and global design variables is made at the top level. Multidisciplinary analysis is made at a each optimization iteration. """ def __init__( self, disciplines, objective_name, design_space, maximize_objective=False, main_mda_class="MDAChain", sub_mda_class="MDAJacobi", **mda_options ): """ Constructor, initializes the objective functions and constraints :param main_mda_class: classname of the main MDA, typically the MDAChain, but one can force to use MDAGaussSeidel for instance :type main_mda_class: str :param disciplines: the disciplines list. :type disciplines: list(MDODiscipline) :param objective_name: the objective function data name. :type objective_name: str :param design_space: the design space. :type design_space: DesignSpace :param maximize_objective: if True, the objective function is maximized, by default, a minimization is performed. :type maximize_objective: bool :param sub_mda_class: the type of MDA to be used, shall be the class name. (default MDAJacobi) :type sub_mda_class: str :param mda_options: options passed to the MDA at construction """ super(MDF, self).__init__( disciplines, objective_name, design_space, maximize_objective=maximize_objective, ) self.mda = None self._main_mda_class = main_mda_class self._mda_factory = MDAFactory() self._instantiate_mda(main_mda_class, sub_mda_class, **mda_options) self._update_design_space() self._build_objective()
[docs] def get_top_level_disc(self): return [self.mda]
def _instantiate_mda( self, main_mda_class="MDAChain", sub_mda_class="MDAJacobi", **mda_options ): """Create MDA discipline""" if main_mda_class == "MDAChain": mda_options["sub_mda_class"] = sub_mda_class self.mda = self._mda_factory.create( main_mda_class, self.disciplines, **mda_options )
[docs] @classmethod def get_sub_options_grammar(cls, **options): """ When some options of the formulation depend on higher level options, a sub option schema may be specified here, mainly for use in the API :param options: options dict required to deduce the sub options grammar :returns: None, or the sub options grammar """ main_mda = options.get("main_mda_class") if main_mda is None: raise ValueError( "main_mda_class option required \n" + "to deduce the sub options of MDF !" ) factory = MDAFactory().factory return factory.get_options_grammar(main_mda)
[docs] @classmethod def get_default_sub_options_values(cls, **options): """ When some options of the formulation depend on higher level options, a sub option defaults may be specified here, mainly for use in the API :param options: options dict required to deduce the sub options grammar :returns: None, or the sub options defaults """ main_mda = options.get("main_mda_class") if main_mda is None: raise ValueError( "main_mda_class option required \n" + "to deduce the sub options of MDF !" ) factory = MDAFactory().factory return factory.get_default_options_values(main_mda)
def _build_objective(self): """Builds the objective function on the MDA""" # Build the objective from the mda and the objective name self._build_objective_from_disc(self._objective_name, discipline=self.mda)
[docs] def get_expected_workflow(self): return self.mda.get_expected_workflow()
[docs] def get_expected_dataflow(self): return self.mda.get_expected_dataflow()
def _update_design_space(self): """Update the design space by removing the coupling variables""" self._set_defaultinputs_from_ds() # No couplings in design space (managed by MDA) self._remove_couplings_from_ds() # Cleanup self._remove_unused_variables() def _remove_couplings_from_ds(self): """Removes the coupling variables from the design space""" design_space = self.opt_problem.design_space for coupling in self.mda.strong_couplings: if coupling in design_space.variables_names: design_space.remove_variable(coupling)