Source code for gemseo.problems.scalable.parametric.problem

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry,
# 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
# Lesser General Public License for more details.
# 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 - initial API and implementation and/or initial
#                         documentation
#        :author: Matthias De Lozzo
Scalable problem
from __future__ import division, unicode_literals

import logging

from gemseo.algos.parameter_space import ParameterSpace
from gemseo.problems.scalable.parametric.core.problem import TMProblem
from gemseo.problems.scalable.parametric.core.variables import get_u_local_name
from gemseo.problems.scalable.parametric.disciplines import (

LOGGER = logging.getLogger(__name__)

MDA_TOLERANCE = {"tolerance": 1e-14, "linear_solver_tolerance": 1e-14}
    "xtol_rel": 1e-4,
    "ftol_rel": 1e-4,
    "xtol_abs": 1e-4,
    "ftol_abs": 1e-4,
    "ineq_tolerance": 1e-3,
    "eq_tolerance": 1e-3,


COUPLING_DIR = "coupling"
COEFF_DIR = "coefficients"
OPTIM_DIR = "opthistoryview"

[docs]class TMScalableProblem(TMProblem): """The scalable problem from Tedford and Martins, 2010, builds a list of strongly coupled scalable disciplines completed by a system discipline computing the objective function and the constraints. These disciplines are defined on an unit design space (parameters comprised in [0, 1]). """ @classmethod def _create_main_model(cls, c_constraint, default_inputs): """Create main model. :param ndarray c_constraint: coefficients for constraint. :param dict(ndarray) default_inputs: default inputs. """ return TMMainDiscipline(c_constraint, default_inputs) @classmethod def _create_sub_model(cls, index, c_shared, c_local, c_cpl, default_inputs): """Create sub model. :param int index: model index. :param ndarray c_shared: coefficients for shared design parameters. :param ndarray c_local: coefficients for local design parameters. :param ndarray c_cpl: coefficients for coupling variables. :param dict(ndarray) default_inputs: default inputs. """ return TMSubDiscipline(index, c_shared, c_local, c_cpl, default_inputs) @property def disciplines(self): """Alias for self.models.""" return self.models @property def n_disciplines(self): """Alias for self.n_submodels.""" return self.n_submodels @property def main_discipline(self): """Main disciplines. :return: main discipline. :rtype: TMDiscipline """ return self.models[0] @property def sub_disciplines(self): """Sub-disciplines. :return: list of disciplines. :rtype: list(TMDiscipline) """ return self.models[1:]
[docs] def get_design_space(self): """Get the TM design space. :return: instance of the design space :rtype: DesignSpace """ d_s = super(TMScalableProblem, self).get_design_space() design_space = ParameterSpace() for name in d_s.names: size = d_s.sizes[name] l_b = d_s.lower_bounds[name] u_b = d_s.upper_bounds[name] value = d_s.default_values[name] design_space.add_variable(name, size, "float", l_b, u_b, value) if self._noised_coupling: for index in range(self.n_submodels): name = get_u_local_name(index) size = self.n_coupling distribution = "OTNormalDistribution" design_space.add_random_variable(name, distribution, size[index]) return design_space
[docs] def reset_disciplines(self): """Reset the disciplines, setting n_calls=0, n_calls_linearize=0, exec_time=0 and local_data={}.""" for discipline in self.disciplines: discipline.n_calls = 0 discipline.exec_time = 0.0 discipline.n_calls_linearize = 0 discipline.local_data = {}