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.
- class gemseo.problems.scalable.data_driven.model.ScalableModel(data, sizes=None, **parameters)¶
Build model with original sizes for input and output variables.
Compute lower and upper bounds of both input and output variables.
Normalize dataset from lower and upper bounds.
Evaluate the scalable derivatives.
Evaluate the scalable function.
- ABBR = 'sm'¶
- property original_sizes¶
Original sizes of variables.
original sizes of variables.
- Return type: