  Optimal actuator and control design is studied as a multi-level optimisation
problem, where the actuator design is evaluated based on the performance of the
associated optimal closed loop. The evaluation of the optimal closed loop for a
given actuator realisation is a computationally demanding task, for which the
use of a neural network surrogate is proposed. The use of neural network
surrogates to replace the lower level of the optimisation hierarchy enables the
use of fast gradient-based and gradient-free consensus-based optimisation
methods to determine the optimal actuator design. The effectiveness of the
proposed surrogate models and optimisation methods is assessed in a test
related to optimal actuator location for heat control.
