This module implements the abstract concept of scalable model which is used by scalable disciplines. A scalable model is built from an input-output learning dataset associated with a function and generalizing its behavior to a new user-defined problem dimension, that is to say new user-defined input and output dimensions.
The concept of scalable model is implemented
ScalableModel, an abstract class which is instantiated from:
data provided as a
variables sizes provided as a dictionary whose keys are the names of inputs and outputs and values are their new sizes. If a variable is missing, its original size is considered.
Scalable model parameters can also be filled in. Otherwise, the model uses default values.
ScalableDiagonalModel class overloads
- class gemseo.problems.scalable.data_driven.model.ScalableModel(data, sizes=None, **parameters)[source]¶
Compute lower and upper bounds of both input and output variables.
Evaluate the scalable derivatives.
Evaluate the scalable function.
- ABBR = 'sm'¶
- property original_sizes¶
Original sizes of variables.
original sizes of variables.
- Return type: