###### Abstract  
Lamb waves offer a series of desirable features for Structural Health Monitoring (SHM) applications, such as the ability to detect small defects, allowing to detect damage at early stages of its evolution. On the downside, their propagation through media with multiple geometrical features results in complicated patterns, which complicate the task of damage detection, thus hindering the realization of their full potential. This is exacerbated by the fact that numerical models for Lamb waves, which could aid in both the prediction and interpretation of such patterns, are computationally expensive. The present paper provides a flexible surrogate to rapidly evaluate the sensor response in scenarios where Lamb waves propagate in plates that include multiple features or defects. To this end, an offline-online ray tracing approach is combined with Frequency Response Functions (FRFs) and transmissibility functions. Each ray is thereby represented either by a parametrized FRFs, if the origin of the ray lies in the actuator, or by a parametrized transmissibility function, if the origin of the ray lies in a feature. By exploiting the mechanical properties of propagating waves, it is possible to minimize the number of training simulations needed for the surrogate, thus avoiding the repeated evaluation of large models. The efficiency of the surrogate is demonstrated numerically, through an example, including different types of features, in particular through holes and notches, which result in both reflection and conversion of incident waves. For most sensor locations, the surrogate achieves an error between 1% and 4%, while providing a computational speedup of three to four orders of magnitude. fontsize=, frames=single-valued-output, frames=single-valued  
flexibility allows for monitoring of various defects, such as fatigue cracks [4; 5], notches [6; 7], delaminations [8; 9], or corrosion [10]. Due to their multi-modality and their dispersive nature, Lamb waves form complex patterns. To simplify these patterns, many Lamb wave-based SHM schemes rely on baseline subtraction to obtain only the response influenced by scattering from defects [11]. In order to keep the scattering response simpler [12], the excitation of single modes is often attempted, which can be achieved either through specially designed actuator setups [13] or through specific frequency tuning [14]. However, the localization of defects still calls for advanced analysis methods, which can be largely classified into data-driven and model-based classes. Popular data-driven methods include Delay-And-Sum (DAS) [15; 16; 17] approaches and the Sparse Reconstruction (SR) [18; 19], while further data-driven methods rely on the Time-Reversal Method (TRM) [20] and the Time of Flight (ToF) [21; 22]. More recently, deep learning (DL) approaches have gained ground with respect to more essential data-driven schemes. A comparison of deep learning models developed for the specific task of GW-based defect detection, including Dense Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks is given by [PERSON] and [PERSON] [23]. In a relevant application which considers a single delamination, [PERSON] et al. [24] used a total of ten Carbon Fiber Reinforced Polymer (CFRP) laminates--nine damaged and one healthy--to train their 1D-CNN. A laser sequentially creates a localized thermal expansion, allowing for broadband excitation in a grid of points. This excitation, in combination with piezoelectric sensors, positioned in three locations, allows for recording of an experimental dataset with more than 2 million measurements. Using this dataset and hyperparameter optimization, the 1D-CNN proves to offer accurate delamination classification from raw signal data. The aspect of training is particularly important in establishing robust DL-based schemes. In this vein, [PERSON] et al. [25] introduced a two-step detection and localization approach.