Add tests for varying parameter fits/optimisations

[BradyPlanden]

Feature description

Provides better coverage for the challenges in optimisation a cost function for differing parameter types. A good example of this is add geometric parameters into our current fitting API (see #18):

import pybop
import numpy as np

parameter_set = pybop.ParameterSet("pybamm", "Chen2020")
model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)

# Fitting parameters
parameters = [
    pybop.Parameter(
        "Positive particle radius [m]",
        prior=pybop.Gaussian(1e-6, 0.0),
        bounds=[0.999e-6, 1.0001e-6],
    ),
    pybop.Parameter(
        "Positive electrode diffusivity [m2.s-1]",
        prior=pybop.Gaussian(3.43e-15, 1e-15),
        bounds=[1e-15, 5e-15],
    ),
]

sigma = 0.001
t_eval = np.arange(0, 900, 2)
values = model.predict(t_eval=t_eval)
corrupt_voltage = values["Terminal voltage [V]"].data + np.random.normal(
    0, sigma, len(t_eval)
)

dataset = [
    pybop.Dataset("Time [s]", t_eval),
    pybop.Dataset("Current function [A]", values["Current [A]"].data),
    pybop.Dataset("Terminal voltage [V]", corrupt_voltage),
]

# Generate problem, cost function, and optimisation class
problem = pybop.Problem(model, parameters, dataset)
cost = pybop.SumSquaredError(problem)
optim = pybop.Optimisation(cost, optimiser=pybop.GradientDescent)
optim.optimiser.set_learning_rate(0.025)

x, final_cost = optim.run()
print("Estimated parameters:", x)

This results in non-scalar geometric parameter when building the PyBaMM model. While we are capturing this issue in #18, adding a larger variety of fitting/optimisation tests will help us check potential edge-cases.

Motivation

No response

Possible implementation

No response

Additional context

No response

[BradyPlanden]

Updating priority on this issue, a move towards diversifying our test suite would be very welcome. This can be done by updating the parameter identification tests we currently have, or by adding an additional test that iterates over a list of parameters to identify.