## 2 Materials and Methods
### Inverse Parameter Identification of the Acoustic Model Parameters  
For the modeling of the porous material within this study, the JCA model is used. It is a five-parameter model, with the model parameters flow resistivity \(\Xi\), porosity \(\Phi\), the tortuosity \(\alpha_{\infty}\) and the viscous and thermal characteristic lengths \(\Lambda,\Lambda^{\prime}\). Within the preceding study, the Johnson-Champoux-Allard-Lafarge (JCAL) model was used that adds the static thermal permeability \(k_{0}^{\prime}\) as a sixth parameter. However, during preliminary tests for this contribution it was observed that the JCA model reaches an acceptable representation of the measurements results as well. Moreover, in another study available in the literature, this model is applied for a very similar generic and additively manufactured porous material as well [52]. However, the three-parameter Miki model was observed to not reach an acceptable representation of the material behavior. Therefore, here, the (compared to the JCAL model) slightly simpler JCA model is used. Since the model parameters cannot be measured directly (with the methods available to the authors), an inverse parameter identification procedure is used to determine the parameters \(\Phi\), \(\alpha_{\infty}\) and \(\Lambda,\Lambda^{\prime}\). The flow resistivity \(\Xi\) is measured, and can thus be used as an input parameter to the inverse procedure. As already mentioned, the procedure is completely described in the preceding contribution [36], and thus is summarized here. The inverse procedure is based on modeling the effect of the porous material as an equivalent fluid using the JCA model in front of an impervious and rigid wall under plane wave loading. This setting resembles the measurement of the absorption coefficient in the impedance tube. Using this model, the absorption coefficient for a given set of model parameters can be predicted. A genetic algorithm is used to adjust the model parameters, such that the error between the measured absorption coefficient (see Figure 2b) and the absorption coefficient predicted by the model is minimized. Therefore, the differences for all measured/predicted frequencies are weighted equally, and the error is reported as the sum of squared differences. The flow resistivity, as obtained from the measurement, remains unchanged during the optimization process. Finally, the model parameters that fit the measurement best are obtained. This procedure is undergone ten times for each specimen. Since the genetic algorithm that is used for the parameter identification is of stochastic nature, the obtained results differ slightly from run to run. Thus, data augmentation is achieved, since for all inputs (the geometry parameters), a set of ten slightly different model parameters are obtained. In total, a dataset comprising 500 pairs of geometry values and JCA model parameters is obtained. However, it should be noted that these parameters only represent a best fit, and therefore are not necessarily physically correct. In order to investigate the general validity of the resulting parameters, a plausibility check for the porosity is shown in the preceding study [36]. For the investigated generic porous specimens, the porosity can be calculated approximately based on the bar geometry by \(\Phi\approx\frac{s}{s+d}\). These results are compared to the parameter values resulting from the optimization process, and are in reasonable agreement with the analytic prediction. Thus, it is assumed that the parameter values resulting from the optimization process are a reasonable estimate of the correct physical values. Modeling the Relation of JCA Model Parameters and Microscale Geometry Using an Artificial Neural Network  
After the inverse parameter identification, for each specimen, the geometry parameters (from manufacturing) and the corresponding JCA model parameters are known.