### Characterization and Design of Porous Materials  
Common to all porous media material models is the aforementioned homogenization. Thus, the models assume a material specimen that is large (in a geometrical sense) in comparison to the material's structure on the microscale. Moreover, the microscale geometry needs to be small compared to the acoustic wavelength [28]. Accordingly, the microscale geometry and, in general, its influence on the parameters of the acoustic model, is taken into account only implicitly when modeling the material. However, this is a major drawback for the goal of tailoring a material to specific needs. Here, tailoring a material is understood as designing and manufacturing a material that exhibits a prescribed behavior, e.g., an absorption coefficient or surface impedance. For tailoring a material, two prerequisites are required: first, the acoustic model parameters and a description of the microscale (for manufacturing purposes) are needed; second, the relationship between these quantities needs to be understood.  
The acoustic model parameters can either be measured or obtained by means of inverse parameter identification. The direct measurement is, to the best of the author's knowledge, possible only for the flow resistivity, the porosity and (for electrically non-conductive materials) the tortuosity [28]. By means of inverse parameter identification, the aforementioned parameters, as well as other parameters, can be obtained. Therefore, the material is modeled and, given a specific cost function, e.g., a specified absorption coefficient, the parameters of interest are inversely computed, if required using an optimization algorithm, e.g., see [29; 30; 31]. A method capable of recovering all six parameters of the JCAL model from impedance tube measurements is introduced in [32]. On the other side, the microscale geometry of a given porous material can be obtained, e.g., by means of computer tomography (CT) scans, as shown in [33; 34]. Additionally, CT scans can support the identification of porous material model parameters when combined with a pore-resolved flow simulation, as shown in [35]. Therefore, a flow simulation of the fluid flow in the pores is conducted using the Lattice-Boltzmann method allowing for a direct computation of flow resistivity, tortuosity and the characteristic lengths. In summation, several approaches exist that allow us to obtain a dataset of the microscale geometry and acoustic model parameters of porous materials, i.e., the first prerequisite for tailoring a porous material.  
The second prerequisite, i.e., the relationship between the microscale geometry and the acoustic model parameters, is more challenging. For special microscale geometries, e.g., (slanted) cylindrical pores, the flow resistivity and tortuosity can be derived analytically, as shown in [23]. However, to the best of the author's knowledge, no models exist in the literature that allow us to infer the acoustic model parameters of a given or desired material based on the microscale geometrical parameters for a general material. To circumvent this issue, in a preceding study, we show the application of a machine-learning based approach to model the relation between the microscale geometrical parameters and the acoustic model parameter of a generic porous material [36]. To do so, generic porous material specimens were produced using additive manufacturing, and two different machine-learning models were trained to predict the acoustic model parameters of the JCAL model based on geometrical parameters describing the microscale geometry. The acoustic model parameters were obtained using an inverse parameter identification procedure and the geometrical parameters were known because the materials were manufactured beforehand using additive manufacturing. The resulting machine-learning models were used to close an optimization loop to generate specimens with a desired absorption coefficient.