## 5 Conclusions and Future Works  
In this study, we have introduced a framework aimed at facilitating the transfer of features from higher to lower fidelity models, specifically from model-scale tests to simulations. This framework holds promises for enhancing simulation accuracy in ML applications, particularly in the domain of hydrodynamic responses of floating units, by injecting productive entropy into simulation data to mimic real-world features. To achieve this, we streamlined the parameters involved, reducing complications, focusing on selecting simple geometrical models, basic mooring systems, limiting wave direction angles, and wave heights. These intentional limitations were imposed to gain better control over influential factors, although the impact of other parameters needs further investigation within the framework. The setup operates by subjecting time series data obtained from experimental tests and simulations to wavelet convolution operations at different scales, extracting fine features for mapping to exciting wave forces. The data are then compressed in the temporal direction through averaging and normalization of obtained coefficients, resulting in a pixel-intensity vector representing 40 s features within the data. This vector is then projected into planes for better scrutiny and observation of features across different scenarios. The feature transfer operation has been executed within the new framework, which is rooted in the fundamentals of data-driven modeling. Initially, the concept was tested using deterministic data and later expanded to incorporate stochastic data for a comprehensive evaluation. Our results within limited scenarios have shown that the proposed framework is effective for close spectral ranges and can significantly improve simulation data accuracy/entropy in the spectral range of interest for high-frequency content. However, its capability diminishes when applied to distant frequencies. Thus, the framework is best suited for augmenting features of simulation data for close spectral ranges. Nevertheless, it is important to stress that sea waves are intrinsically narrow spectrum in nature, making the proposed engineering beneficial for practical hydrodynamic studies. The significance and novelty of this study reside in two key areas. Firstly, given that simulation data offer the most feasible approach for the data generation process aiming at feeding ML, the proposed framework represents a significant step towards mimicking real-world features and establishing robust hybrid modeling. Although the current focus has been on utilizing the features directly, it is worth noting that these features can also be reversed into time series and applied for various hydrodynamics or seakeeping purposes. Additionally, the framework allows for the monitoring of densified feature spread based on different inputs to the system. This enables users to visualize how training occurs behind the scenes for ML networks by observing feature cluster patterns according to different inputs, leading to the development of more efficient ML models. Furthermore, as discussed, the proposed mechanism significantly reduces hardware and software computational resources for response classification, which appears as one of the neural-based networks limitations [33]. This topic will be further explored in part B of the current issue. In a broader context, our findings hold significance not only in hydrodynamic responses of floating bodies but also in other modeling fields involving systems excited by sequential deterministic or stochastic inputs. The proposed framework can be tailored to various engineering disciplines, including aerospace, civil, biomedical, and environmental engineering, among others. For future studies, it would be beneficial to explore the impact of geometrical dimension, especially when mimicking a ship. One approach could be to use a rectangular cubic model to assess how different geometries affect the features extracted by the framework.