A controllability method for Maxwell's equations

TL;DR

This paper introduces a controllability-based numerical method for efficiently solving time-harmonic Maxwell's equations by minimizing a quadratic cost functional, leveraging existing time-domain solvers for fast convergence.

Contribution

It presents a non-intrusive, scalable algorithm that automatically inherits the parallelism of time-domain solvers and efficiently recovers time-harmonic solutions from periodicity minimization.

Findings

Algorithm significantly speeds up convergence to time-harmonic solutions.

Method easily integrates with existing simulation software.

Numerical examples demonstrate improved efficiency over traditional time-marching methods.

Abstract

We propose a controllability method for the numerical solution of time-harmonic Maxwell's equations in their first-order formulation. By minimizing a quadratic cost functional, which measures the deviation from periodicity, the controllability method determines iteratively a periodic solution in the time domain. At each conjugate gradient iteration, the gradient of the cost functional is simply computed by running any time-dependent simulation code forward and backward for one period, thus leading to a non-intrusive implementation easily integrated into existing software. Moreover, the proposed algorithm automatically inherits the parallelism, scalability, and low memory footprint of the underlying time-domain solver. Since the time-periodic solution obtained by minimization is not necessarily unique, we apply a cheap post-processing filtering procedure which recovers the time-harmonic solution from any minimizer. Finally, we present a series of numerical examples which show that our algorithm greatly speeds up the convergence towards the desired time-harmonic solution when compared to simply running the time-marching code until the time-harmonic regime is eventually reached.