## 4 Artificial Intelligence Methods in Metasurface Design  
Modern metasurface research is increasingly using advanced artificial intelligence methods to address the challenges of designing complex structures at the microscopic level.  
These methods make it possible to automatically generate and optimize the geometry of the unit cells of metasurfaces, which is crucial for achieving the desired electromagnetic properties [61].  
[PERSON] et al. [62] showed a specific approach in metasurface design. In traditional metasurface design, full-wave simulations are commonly employed to determine parameters like the amplitude and phase of meta-atoms. In practical scenarios where meta-atoms interact with various neighboring atoms, near-field coupling effects can lead to deviations that affect the metasurface's overall performance. AI tackles this by directly considering these coupling effects using data, which enhances the accuracy of the calculations and provides a deeper understanding of the physical processes occurring in the metasurfaces. This makes AI an invaluable tool for optimizing and improving metasurface design and performance while also opening up new avenues for research and future applications.  
[PERSON] et al. [63] conducted a review of AI-based metamaterial design. They highlighted that conventional design approaches often involve manual processes that are both time-consuming and demanding of significant resources. Incorporating artificial intelligence (AI) into the optimization of metamaterial design can help explore various disciplines and overcome design challenges. AI-driven metamaterial design also opens the door to creating new materials by fine-tuning design parameters in a way that traditional methods cannot achieve. Additionally, AI can be used to expedite the analysis of large datasets and to maximize the potential of limited data through generative models.  
[PERSON] et al. [64] highlighted some significant fields of AI-based metasurfaces that could be desirable in the process of designing the metasurfaces. AI techniques, particularly machine learning algorithms, have been shown to be effective in optimizing metasurface designs by identifying and fine-tuning parameters that are difficult to manage using traditional methods. AI also excels in analyzing large and complex datasets, which is crucial in metasurface design due to the intricate relationships between the design variables. AI can extract meaningful patterns from these datasets to guide the design process more effectively. Generative models, such as conditional generative adversarial networks (GANs), are particularly effective for creating new metasurface designs. These models can generate novel configurations that meet specific design criteria. AI also enables real-time analysis and feedback during the design process, allowing for rapid iteration and refinement of the metasurface structures. This can reduce the time required for the development process of new metasurface designs.  
Overall, the integration of AI into metasurface design processes not only enhances the efficiency and creativity of the designs but also opens new avenues for research and application by addressing the limitations of traditional design methodologies [64]. A desirable place to use AI in the metasurface domain for the purpose of design is geometry. This paper demonstrates the application of machine learning methods in this area only.