Reflectarray antenna design for X band applications using artificial intelligence

This thesis introduces an innovative method for designing reflectarray antennas specifically for X-band applications, utilizing the capabilities of artificial intelligence (AI) and surrogate modeling. Reflectarray antennas are crucial in contemporary communication systems, especially in satellite and radar applications, due to their capacity to influence electromagnetic waves by adjusting phase shifts. However, their design presents substantial hurdles due to the complex interaction of multiple physical parameters and the high computer resources required for precise simulation and optimization. The intricacy in the design of reflectarray antennas stems from the requirement to accurately manipulate the phase of the reflected waves, which is crucial for attaining the required emission patterns. Conventional approaches to design and optimization are frequently laborious and require significant computational resources, mainly because of the iterative nature of the optimization process and the large number of variables in the design space. This thesis tackles these difficulties by providing AI-powered surrogate models as an innovative approach. The utilization of AI surrogate models in this particular situation has numerous innovative benefits. Firstly, these models greatly decrease the computational burden by approximating the behavior of reflectarray antennas, hence facilitating quicker and more efficient design cycles. Furthermore, AI models offer a clearer comprehension of the complex connections between design elements and antenna performance, making it easier to identify the most efficient configurations. The implementation of AI in this field allows for the real-time adjustment of antenna properties, which is crucial in dynamic operating settings. To summarize, this thesis showcases the practicality and efficiency of AI surrogate models in the development and enhancement of reflectarray antennas for X-band applications.