Multi-level Optimal Control with Neural Surrogate Models
<|reference_start|>Multi-level Optimal Control with Neural Surrogate Models: 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.<|reference_end|>
@article{kalise2024multi-level,
    title={Multi-level Optimal Control with Neural Surrogate Models},
    author={Dante Kalise, Estefan'ia Loayza-Romero, Kirsten A. Morris, Zhengang
  Zhong},
    journal={arXiv preprint arXiv:2402.07763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07763},
    primaryClass={math.OC cs.LG cs.NA math.NA}
}