Expedited Gradient-Based Design Closure of Antennas Using Variable-Resolution Simulations and Sparse Sensitivity Updates

Numerical optimization has been playing an increasingly important role in the design of contemporary antenna systems. Due to the shortage of design-ready theoretical models, optimization is mainly based on electromagnetic (EM) analysis, which tends to be costly. Numerous techniques have evolved to abate this cost, including surrogate-assisted frameworks for global optimization, or sparse sensitivity updates for speeding up local search. In the latter, CPU-heavy updates of the system response sensitivity through finite differentiation are suppressed based on, e.g., the magnitude of design variability during the optimization run. Another approach is to incorporate variable-resolution simulations. Recently, a technique exploiting a continuous spectrum of admissible model fidelity levels has been reported, thereby allowing for a considerable reduction of computational expenditures. Seeking further savings, this work introduces an accelerated gradient-based algorithm with sparse sensitivity updates and variable-resolution EM simulations. Our technique is validated using four broadband antennas, and demonstrated to offer substantial (around 80%) savings over the benchmark while maintaining acceptable design quality.