###### Abstract
They specifically trained their 1D-CNNs on data collected from CFRP plates with attached aluminum disks, which are employed to simulate damage. From this dataset, physical features, such as the time of flight, are extracted. In further work, in an effort to reduce the training data, [PERSON] et al. [26] applied transfer learning with optimal transport on data generated with a numerical model, which was validated with experiments. In the present work, data-driven methods exploiting the principle of Frequency Response Functions (FRFs) or transfer functions are of special interest, as the FRF forms a central concept in the development of the proposed surrogate. [PERSON] et al. [27] use the concept of electrical transfer functions to verify the rationality of their measurements. Electrical transfer functions are further used by [PERSON] and [PERSON] [28], in the context of the Time-Reversal Method. The transfer function between the excitation and the measured signal is modified in order to compensate for the influence of the transducer. A drawback of their method lies in the necessity for mode separation, which is often not feasible, especially if the distance between the actuator and sensor is small. The surrogate proposed in the present paper shows the ability to track individual modes and, therefore, it allows for detection of specific modes, which reflections at different parts of the model stem from. In the Decomposition of the Time-Reversal Operator and MUltiple Signal Classification (DORT-MUSIC) technique, the transfer function is evaluated for a set of sensor-transducer pairs and, based on individual transfer functions, a transfer matrix is constructed. This matrix is decomposed by means of a Singular Value Decomposition (SVD), which allows for extraction of reflectivity characteristic features from signals originating from different sources, e.g., scatterers. To obtain the transfer matrix, [PERSON] and [PERSON] [29] used a sensor setup, where the actuators are perpendicular to the sensors, while [PERSON] et al. [30] relied on a single piezoelectric array. To improve the localization of debonding in CFRP-reinforced steel structures, [PERSON] et al. [31] derived a Waveform Feature Index (WFI) based on changes in the Transfer function. A method to experimentally derive FRF was given by [PERSON] et al. [32], who used the step function as an excitation. They show that this approach is efficient for FRFs up to 900 kHz. A further application of the FRF is in combination with the wave focusing approach. In this approach, the excitation is modified in order to account for the dispersive nature of Lamb waves [33]. Based on the use of the FRF, [PERSON] et al. [34] reduced the number of required physical recordings needed for wave focusing. Furthermore, the utilization of the FRF allows for seamless prediction of the response for varying excitations. This flexibility of the FRF in dealing with various excitations is a key property, which is exploited in the creation of the surrogate proposed in the present paper. Despite their attractive trait in using minimal prior knowledge, data-driven methods often remain limited to the setups they were trained on. To increase the flexibility of SHM schemes, mechanical knowledge in the form of the governing equations of the wave propagation problem can be introduced [35]. Such model-based approaches come with the bottleneck of a high computational time, particularly for Lamb wave problems, as the high frequencies and short wavelengths involved require fine discretizations in space and time for mesh-based solvers [3; 36]. As a consequence of the high associated computational cost, inverse algorithms need to be efficient with respect to the number of required evaluations of the model. Such an approach was introduced by [PERSON] et al. [37], who performed Full Waveform Inversion (FWI) for the 1D wave equation. They introduced a scaling factor for the density of the structure, with the scaling factor tending to 0 for void regions and 1 for solid regions.