## 1 Introduction  
Ultrasonic waves are constrained by the boundaries when propagating in plate and shell structures, which exist in the form of ultrasonic guided waves [1]. The application of ultrasonic guided waves enables large-scale and efficient detection of surface and internal damage in structures [2, 3, 4, 5, 6, 7]. Researchers have developed various methods for ultrasonic guided wave damage imaging, focusing on how to effectively excite and sense guided wave signals as well as extract and analyze the characteristics of weak damage signals. Ultrasonic imaging methods based on array-excitation/sensing typically arrange piezoelectric wafer units with a specific spatial distribution. By analyzing the interaction between the wave field, characteristics of the wave sources can be obtained; thereby, damage localization and imaging is achieved. The complete reconstruction of the guided wave field information relies on the propagation characteristics of ultrasonic guided waves within the structure. The accuracy and resolution of different imaging techniques depend on the various parameters that can be obtained, which are constrained by spatial/time sampling rates, complex wave field characteristics, and detection environment limitations. Traditional ultrasonic guided wave damage imaging methods using array sensing include the Synthetic Aperture Focusing Technique (SAFT) [8; 9; 10], the Total Focusing Method (TFM) [11; 12; 13; 14; 15] based on the delay-and-sum (DAS) algorithm, and the time-reversal (TR) imaging method based on the inversion concept of guided wave field propagation, among others [16; 17; 18; 19; 20]. The integration of artificial intelligence and traditional ultrasonic guided wave imaging can significantly enhance the defect characterization capabilities of ultrasonic imaging methods. [PERSON] et al. [7] proposed a CNN-based beamforming network that combines with DAS, enabling high-resolution, high-SNR Lamb wave damage imaging while maintaining computational efficiency. [PERSON] et al. [21] proposed a novel noncontact super-resolution guided wave array imaging approach using deep learning, combining global detection and local super-resolution fully convolutional networks to visualize subwavelength defects in plate-like structures with high resolution and noise robustness. These advancements broaden our understanding of the resolution performance of different ultrasonic guided wave imaging methods as well as their imaging capabilities in noisy environments. Meanwhile, we still need to further investigate physics-driven guided wave imaging methods as physical benchmarks and sources of interpretability for data-driven models, promoting a symbiotic framework integrating physics-based priors with data-driven techniques. Compared to the single-input-single-output data relied upon by SAFT, the more comprehensive, all possible multi-input-multi-output data considered by TFM can provide higher resolution imaging for damage detection. The DAS algorithm controls the relative delays of the excitation signals for each driving/sensing element through a multi-channel control unit, allowing the superposition of multiple elements to form a directional probing beam that can be deflected or focused in a specified direction. This method combines the advantages of acoustic field control scanning with long-distance probing of ultrasonic guided waves, making it one of the ideal means for rapid detection of damage in plate-like structures. From the perspective of wave field inversion, the ray approximation of DAS simplifies the interaction process between the guided wave field and the damage to the incident/reflection process of the guided waves in the propagation direction. Under the assumption of constant wave speed, the scattering time information of the damage is used to infer the spatial location of the scattered waves. Therefore, the accurate acquisition of the time-amplitude information of the scattered waves is key to improving imaging accuracy. [PERSON] et al.