# surrogate_model.py¶

Class definition for SurrogateModel, the base class for all surrogate models.

class openmdao.surrogate_models.surrogate_model.MultiFiSurrogateModel[source]

Base class for surrogate models using multi-fidelity training data.

__init__()

Initialize all attributes.

linearize(x)

Calculate the jacobian of the interpolant at the requested point.

Parameters: x : array-like Point at which the surrogate Jacobian is evaluated.
predict(x)

Calculate a predicted value of the response based on the current trained model.

Parameters: x : array-like Point(s) at which the surrogate is evaluated.
train(x, y)[source]

Calculate a predicted value of the response based on the current trained model.

Parameters: x : array-like Point(s) at which the surrogate is evaluated. y : array-like Model responses at given inputs.
train_multifi(x, y)[source]

Train the surrogate model, based on the given multi-fidelity training data.

Parameters: x : list of (m samples, n inputs) ndarrays Values representing the multi-fidelity training case inputs. y : list of ndarray output training values which corresponds to the multi-fidelity training case input given by x.
vectorized_predict(x)

Calculate predicted values of the response based on the current trained model.

Parameters: x : array-like Vectorized point(s) at which the surrogate is evaluated.
class openmdao.surrogate_models.surrogate_model.SurrogateModel[source]

Bases: object

Base class for surrogate models.

Attributes

 trained (bool) True when surrogate has been trained.
__init__()[source]

Initialize all attributes.

linearize(x)[source]

Calculate the jacobian of the interpolant at the requested point.

Parameters: x : array-like Point at which the surrogate Jacobian is evaluated.
predict(x)[source]

Calculate a predicted value of the response based on the current trained model.

Parameters: x : array-like Point(s) at which the surrogate is evaluated.
train(x, y)[source]

Train the surrogate model with the given set of inputs and outputs.

Parameters: x : array-like Training input locations y : array-like Model responses at given inputs.
vectorized_predict(x)[source]

Calculate predicted values of the response based on the current trained model.

Parameters: x : array-like Vectorized point(s) at which the surrogate is evaluated.