## 4 Discussion  
In Section 3.1 the results for the first-order and total sensitivity coefficients for the microscale geometry parameters (bar width \(d\), bar spacing \(s\), bar height \(h\), plane angle \(\varphi\)) with respect to the acoustic model parameters (flow resistivity \(\Xi\), porosity \(\Phi\), tortuosity \(\alpha_{\infty}\), viscous and thermal characteristic lengths \(\Lambda\), \(\Lambda^{\prime}\)) are presented. The results for the second-order effects are shown in Section 3.2. Generally, the observed direct impacts of the geometrical parameters on the acoustic model parameters as well as interaction effects seem plausible. Next, the findings shown in Section 3 shall be compared to similar investigations available in the literature. Although, to the best of the author's knowledge, GSA has not been applied yet to explain machine-learning models that model porous materials, it has been applied in [60] to pore-network model descriptions of a complex polyurethane (PU) foam. In that study, the authors describe the foam on the microscale by a set of geometric parameters, among which the mean pore radius \(\overline{R_{p}}\), the mean throat radius \(\overline{R_{t}}\) and the mean frame thickness \(\overline{T_{f}}\) appear to be most comparable to geometry descriptions used in the study presented here. The acoustic properties of the PU foam are described using the JCA model. The authors report the first-order sensitivity index for all JCA model parameters with respect to the microscale geometry of the foam. Although the PU foam is far more complex in shape and random in the geometric distribution, comparable findings to the study presented here are reported by the authors. For instance, the flow resistivity of the PU foam is most influenced by the throat radius and pore radius. Assume that the throat radius, i.e., the radius of the small interconnections between pores of the PU foam, is comparable to the bar spacing, and the pore radius is comparable to the sum of bar spacing and half of the bar width. This analogue is used in a similar way in [61]. With this assumption, it can be verified in Figure 5a that these quantities are the most influential parameters for the flow resistivity in our generic porous materials. However, the relative influence is inverted. For the foam, the pore size has the largest influence and the throat radius ranks second. Here, the bar width has the highest influence and the bar spacing ranks second. This inversion of the influence is probably due to the rather different geometrical nature of the foam and the generic material presented here. Another comparable finding can be verified for the porosity. The most influencial first-order effect regarding the porosity in Figure 5a is the bar spacing. In [60] the authors show that for the PU foam, the porosity is equally influenced by both the throat radius (that is assumed here to be comparable with the bar spacing) and the mean frame thickness. The frame thickness has no direct analogue in our generic material. However, the comparable influence of the bar spacing/the throat thickness on the porosity can be verified in both studies. A third similarity is the generally large influence of the throat thickness on all studied acoustic model parameters of the PU foam except from the tortuosity. This can be verified for our material as well, as the bar spacing appears to have an impact on all model parameters. Although not based on sensitivity analysis, from [52], comparable findings can be drawn as well. The authors investigate a generic additively manufactured material very similar to the material we investigated. They also model the material based on the JCA model and derive the JCA model parameters using inverse parameter identification similar to the approach we applied in [30; 36]. The authors vary the overall dimensions of the printed specimens measured by the size of the representative elementary volume (REV).