pybop.problems.fitting_problem#

Classes#

FittingProblem

Problem class for fitting (parameter estimation) problems.

Module Contents#

class pybop.problems.fitting_problem.FittingProblem(model: pybop.BaseModel, parameters: pybop.parameters.parameter.Parameters, dataset: pybop.Dataset, check_model: bool = True, signal: list[str] | None = None, additional_variables: list[str] | None = None, initial_state: dict | None = None)[source]#

Bases: pybop.BaseProblem

Problem class for fitting (parameter estimation) problems.

Extends BaseProblem with specifics for fitting a model to a dataset.

Parameters:

model (object) – The model to fit.
parameters (pybop.Parameter or pybop.Parameters) – An object or list of the parameters for the problem.
dataset (dictionary) – Dataset object containing the data to fit the model to.
check_model (bool, optional) – Flag to indicate if the model should be checked (default: True).
signal (str, optional) – The variable used for fitting (default: “Voltage [V]”).
additional_variables (list[str], optional) – Additional variables to observe and store in the solution (default additions are: [“Time [s]”]).
initial_state (dict, optional) – A valid initial state, e.g. the initial open-circuit voltage (default: None).
Attributes (Additional)
---------------------
dataset – The dictionary from a Dataset object containing the signal keys and values to fit the model to.
domain_data (np.ndarray) – The domain points in the dataset.
n_domain_data (int) – The number of domain points.
target (np.ndarray) – The target values of the signals.

_evaluateEIS(inputs: pybop.parameters.parameter.Inputs, update_capacity=False) → dict[str, numpy.ndarray[numpy.float64]][source]#

Evaluate the model with the given parameters and return the signal.

Parameters:: inputs (Inputs) – Parameters for evaluation of the model.
Returns:: y – The simulated model output y(ω) for the given inputs.
Return type:: np.ndarray

evaluate(inputs: pybop.parameters.parameter.Inputs, update_capacity=False) → dict[str, numpy.ndarray[numpy.float64]][source]#

Evaluate the model with the given parameters and return the signal.

Parameters:: inputs (Inputs) – Parameters for evaluation of the model.
Returns:: y – The simulated model output y(t) for self.eis == False, and y(ω) for self.eis == True for the given inputs.
Return type:: np.ndarray

evaluateS1(inputs: pybop.parameters.parameter.Inputs)[source]#

Evaluate the model with the given parameters and return the signal and its derivatives.

Parameters:: inputs (Inputs) – Parameters for evaluation of the model.
Returns:: A tuple containing the simulation result y(t) as a dictionary and the sensitivities dy/dx(t) evaluated with given inputs.
Return type:: tuple[dict, np.ndarray]

set_initial_state(initial_state: dict | None = None)[source]#

Set the initial state to be applied to evaluations of the problem.

Parameters:: initial_state (dict, optional) – A valid initial state (default: None).

_dataset[source]#

_domain_data[source]#

_n_parameters[source]#

n_data[source]#