"""Module for the ParametricProblem class""" from abc import abstractmethod from .abstract_problem import AbstractProblem class InverseProblem(AbstractProblem): """ The class for the definition of inverse problems, i.e., problems with unknown parameters that have to be learned during the training process from given data. Here's an example of a spatial inverse ODE problem, i.e., a spatial ODE problem with an unknown parameter `alpha` as coefficient of the derivative term. :Example: >>> from pina.problem import SpatialProblem, InverseProblem >>> from pina.operators import grad >>> from pina.equation import ParametricEquation, FixedValue >>> from pina import Condition >>> from pina.geometry import CartesianDomain >>> import torch >>> >>> class InverseODE(SpatialProblem, InverseProblem): >>> >>> output_variables = ['u'] >>> spatial_domain = CartesianDomain({'x': [0, 1]}) >>> unknown_parameter_domain = CartesianDomain({'alpha': [1, 10]}) >>> >>> def ode_equation(input_, output_, params_): >>> u_x = grad(output_, input_, components=['u'], d=['x']) >>> u = output_.extract(['u']) >>> return params_.extract(['alpha']) * u_x - u >>> >>> def solution_data(input_, output_): >>> x = input_.extract(['x']) >>> solution = torch.exp(x) >>> return output_ - solution >>> >>> conditions = { >>> 'x0': Condition(CartesianDomain({'x': 0}), FixedValue(1.0)), >>> 'D': Condition(CartesianDomain({'x': [0, 1]}), ParametricEquation(ode_equation)), >>> 'data': Condition(CartesianDomain({'x': [0, 1]}), Equation(solution_data)) """ @abstractmethod def unknown_parameter_domain(self): """ The parameters' domain of the problem. """ pass @property def unknown_variables(self): """ The parameters of the problem. """ return self.unknown_parameter_domain.variables @property def unknown_parameters(self): """ The parameters of the problem. """ return self.__unknown_parameters @unknown_parameters.setter def unknown_parameters(self, value): self.__unknown_parameters = value