Method adjoint_run

python/adjoint/optimization_problem.py:308–357

(self)

                    )  # place the appropriate adjoint sources

    def adjoint_run(self):
        # set up adjoint sources and monitors
        self.prepare_adjoint_run()

        # flip the m number
        if utils._check_if_cylindrical(self.sim):
            self.sim.change_m(-self.sim.m)

        # flip the k point
        if self.sim.k_point:
            self.sim.change_k_point(-1 * self.sim.k_point)

        self.adjoint_design_region_monitors = []
        for ar in range(len(self.objective_functions)):
            # Reset the fields
            self.sim.restart_fields()
            self.sim.clear_dft_monitors()

            # Update the sources
            self.sim.change_sources(self.adjoint_sources[ar])

            # register design dft fields
            self.adjoint_design_region_monitors.append(
                utils.install_design_region_monitors(
                    self.sim,
                    self.design_regions,
                    self.frequencies,
                    self.decimation_factor,
                )
            )
            self.sim._evaluate_dft_objects()

            # Adjoint run
            self.sim.run(
                *self.step_funcs,
                until_after_sources=mp.stop_when_dft_decayed(
                    self.decay_by, self.minimum_run_time, self.maximum_run_time
                ),
            )

        # reset the m number
        if utils._check_if_cylindrical(self.sim):
            self.sim.change_m(-self.sim.m)

        # reset the k point
        if self.sim.k_point:
            self.sim.change_k_point(-1 * self.sim.k_point)

        # update optimizer's state
        self.current_state = "ADJ"

    def calculate_gradient(self):
        # Iterate through all design regions and calculate gradient