access the objective function during optimisation

[poutine-dejeuner]

From the meepcon2022 tutorial notebook, there is a cell in which the optimisation problem is set up. The objective function J is defined. How can I access the value of the objective function J? More generally, I am interested in how to reproduce the same eigenmode computation from a simulation object directly.

TE0 = mpa.EigenmodeCoefficient(sim,
        mp.Volume(center=mp.Vector3(x=-Sx/2 + pml_size + 2*waveguide_length/3),
            size=mp.Vector3(y=1.5)),mode)
TE_top = mpa.EigenmodeCoefficient(sim,
        mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,arm_separation/2,0),
            size=mp.Vector3(y=arm_separation)),mode)
TE_bottom = mpa.EigenmodeCoefficient(sim,
        mp.Volume(center=mp.Vector3(Sx/2 - pml_size - 2*waveguide_length/3,-arm_separation/2,0),
            size=mp.Vector3(y=arm_separation)),mode)
ob_list = [TE0,TE_top,TE_bottom]

def J(source,top,bottom):
    power = npa.abs(top/source) ** 2 + npa.abs(bottom/source) ** 2
    return npa.mean(power)

opt = mpa.OptimizationProblem(
    simulation = sim,
    objective_functions = J,
    objective_arguments = ob_list,
    design_regions = [design_region],
    frequencies=frequencies

[mochen4]

If you already have defined an OptimizationProblem, opt, you can simply call opt() and it will return the objective function value and the gradient with respect to the design variables; you can additionally pass the argument need_gradient=False so that it will only compute the objective function value without the gradient.
Alternatively, with the simulation object sim alone, you can first add the mode monitors with "sim.add_mode_monitor(...)", run the simulation, and then call "sim.get_eigenmode_coefficients(...)" to compute the corresponding values. See the tutorial example https://meep.readthedocs.io/en/master/Python_Tutorials/Mode_Decomposition/