Abstract The increasing energy demand necessitates efficient renewable energy solutions. This necessity highlights solar energy as the most reliable and abundant source. Since material design is very important for obtaining efficient energy from the sun, metamaterial absorbers are of interest. Metamaterials, which are characterized by their negative refractive index and electromagnetic manipulation capabilities, are preferred in solar absorber designs due to their high efficiency, low cost, and ease of fabrication. The characteristic properties of metamaterial absorbers can be obtained by optimizing the parameters in their geometric structures. However, optimizing these structures requires electromagnetic simulations involving a large parameter space. This situation results in high computational costs that require high-performance computing (HPC) resources and parallel processing. To overcome this challenge, computational steps can be significantly reduced by integrating artificial intelligence tools into optimization processes. In this study, a metamaterial absorber operating at ultraviolet, visible light, and infrared wavelengths is designed, and its solar energy collection efficiency is investigated. Then, a deep learning-based optimization approach is presented for the proposed solar energy absorber. The approach proposed in the study is particularly suitable for HPC-supported environments where large-scale simulations can be generated and processed using parallel computing. With the proposed deep neural network optimization model, geometric parameters are suggested for the maximum absorption value to be obtained from the solar absorber. Thanks to this state-of-the-art deep learning-based optimization model, the number of simulations has been reduced by approximately 90%, decreasing from 1256 to 126. Therefore, the approach proposed in the study significantly reduces the computational workload and reveals the potential of combining deep learning methods with HPC infrastructures in accelerating electromagnetic optimization problems.
Karacan et al. (2026) studied this question.