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, domain: 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 (list[str], optional) – A list of variables to fit (default: [“Voltage [V]”]).

  • domain (str, optional) – The name of the domain (default: “Time [s]”).

  • 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) which will trigger a model rebuild on each evaluation. Example: {“Initial open-circuit potential [V]”: 4.1} NOTE: Sensitivities are not support with this arg due to the model rebuilding.

  • 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.

_evaluate(func, inputs, calculate_grad=False) → dict[str, numpy.ndarray[numpy.float64]][source]#

Perform simulation using the specified method and handle exceptions.

Parameters:

  • func (callable) – The method to be used for simulation.

  • inputs (Inputs) – Parameters for evaluation of the model.

Returns:

The simulation result y(t) and, optionally, the sensitivities dy/dx(t) for each parameter x and signal y.

Return type:

dict[str, np.ndarray[np.float64]] or tuple[dict[str, np.ndarray], dict[str, dict[str, np.ndarray]]]

evaluate(inputs: pybop.parameters.parameter.Inputs) → 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:

The simulated model output y(t) for self.eis == False, and y(ω) for self.eis == True for the given inputs.

Return type:

dict[str, np.ndarray[np.float64]]

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) and the sensitivities dy/dx(t) for each parameter x and signal y evaluated with the given inputs.

Return type:

tuple[dict[str, np.ndarray[np.float64]], dict[str, dict[str, np.ndarray]]]

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

Set the initial state to be applied for every problem evaluation.

Parameters:

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

_dataset[source]#
_domain_data[source]#
_n_parameters[source]#
n_data[source]#