## 7 Conclusions  
This study focuses on finding optimal design parameters for a wave absorber using an inclined porous plate, employing an artificial neural network (ANN) model to enhance the operating efficiency of a wave tank by minimizing both the averaged reflection coefficient (\(R_{f}\)) and the spatial footprint (\(l\cos\theta/h\)) of the porous plate. The dual boundary element method (DBEM) was employed as a numerical approach, combined with the quadratic velocity model and linear Darcy's model, to account for energy dissipation through an inclined porous plate. The numerical results were validated through experiments conducted in a two-dimensional wave flume.  
The design parameters influencing the reflection coefficient were chosen to be the inclination angle and the non-dimensional plate length, whereas the key parameter porosity was fixed at 0.1. The training dataset for the ANN model consists of random input features corresponding to these design parameters, along with an output feature of the averaged reflection coefficient, which is determined using the developed DBEM model. The ANN model was effectively trained by optimizing hyperparameters to maximize the \(R^{2}\) score while minimizing both the mean squared error (MSE) and mean absolute error (MAE), leading to a model with high prediction accuracy. Then, the trained ANN model was utilized to make predictions of the averaged reflection coefficient with a larger random input feature dataset. The ANN model reduces the need for extensive numerical calculations while maintaining accuracy. In the optimization process focused on minimizing both wave reflection and spatial footprint, weighting factors were assigned according to their relative importance, utilizing the weighted sum model (WSM) within the multi-criteria decision-making approach. When the optimization is performed with a weighting factor (80% : 20%) that significantly prioritizes minimizing reflection over the spatial footprint, the optimal design parameter with \(l/h=1.46\) and \(\theta=5.34^{\circ}\) seems to be a reasonable choice, effectively balancing both the objectives required in the design of a wave absorber. The ensemble of various methods provided a computationally efficient yet technically robust optimization with a multi-criteria framework that supports objective-driven design, facilitating informed decision making. While the machine learning approach shows great potential, a limited dataset may lead to an underestimation or overestimation of performance. Therefore, additional datasets and experimental studies are needed to validate these findings.  
Conceptualization, I.-H.C.; methodology, S.K.N. and I.-H.C.; software, S.K.N. and I.-H.C.; validation, S.K.N. and I.-H.C.; writing--original draft preparation, S.K.N. and I.-H.C.; writing--review and editing, I.-H.C.; supervision, I.-H.C.; project administration, S.C.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2022R1 IIA3066608).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.