Parallel Multiphysics Optimization for Microwave Devices Exploiting Neural Network Surrogate

This letter proposes a new surrogate-based multiphysics optimization technique for microwave devices incorporating artificial neural networks (ANNs) and trust-region algorithm. In the proposed technique, at each optimization iteration, we build an accurate and efficient ANN surrogate model using multiple multiphysics training samples around the optimized solution from the previous iteration. A parallel data generation technique is exploited to accelerate the optimization process. To improve the convergence of the proposed technique, we use a trust-region algorithm to recalculate the ANN surrogate model range at each optimization iteration. By using the proposed technique, the values of design parameters have a large and effective update toward the optimal solution at each iteration, and the optimization can converge in fewer iterations. Therefore, we can achieve the optimal solution faster than existing multiphysics optimization methods. A waveguide filter using piezo actuator is used as an example to demonstrate the validity of our technique.

Automated summary

This letter introduces a new surrogate-based multiphysics optimization technique for microwave devices incorporating artificial neural networks (ANNs) and a trust-region algorithm. By using a parallel data generation technique and recalculating the range of the ANN surrogate model at each optimization iteration, the values of design parameters are effectively updated towards the optimal solution, leading to faster convergence.