Create a discipline from analytical expressions

from __future__ import annotations

from gemseo import configure_logger
from gemseo import create_discipline
from numpy import array


<RootLogger root (INFO)>


A simple MDODiscipline can be created using analytic formulas, e.g. \(y_1=2x^2\) and \(y_2=5+3x^2z^3\), thanks to the AnalyticDiscipline class which is a quick alternative to model a simple analytic MDO problem.

Create the dictionary of analytic outputs

First of all, we have to define the output expressions in a dictionary where keys are output names and values are formula with string format:

expressions = {"y_1": "2*x**2", "y_2": "5+3*x**2+z**3"}

Create the discipline

Then, we create and instantiate the corresponding AnalyticDiscipline, which is a particular MDODiscipline. For that, we use the API function create_discipline() with:

  • discipline_name="AnalyticDiscipline",

  • name="analytic",

  • expressions=expr_dict.

In practice, we write:

disc = create_discipline("AnalyticDiscipline", expressions=expressions)


GEMSEO takes care of the grammars and MDODiscipline._run() method generation from the expressions argument. In the background, GEMSEO considers that x is a monodimensional float input parameter and y_1 and y_2 are monodimensional float output parameters.

Execute the discipline

Lastly, we can execute this discipline any other:

input_data = {"x": array([2.0]), "z": array([3.0])}
{'x': array([2.]), 'z': array([3.]), 'y_2': array([44.]), 'y_1': array([8.])}

About the analytic jacobian

The discipline will provide analytic derivatives (Jacobian) automatically using the sympy library.

This can be checked easily using MDODiscipline.check_jacobian():

    INFO - 13:54:10: Jacobian: dp y_1/dp x succeeded.
    INFO - 13:54:10: Jacobian: dp y_1/dp z succeeded.
    INFO - 13:54:10: Jacobian: dp y_2/dp x succeeded.
    INFO - 13:54:10: Jacobian: dp y_2/dp z succeeded.
    INFO - 13:54:10: Linearization of MDODiscipline: AnalyticDiscipline is correct.


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