## 4 Discussion  
The analysis of the articles has shown that numerical and analytical methods remain the most frequently used approaches for solving diagnostic tasks. Most of these methods suffer from issues related to computational costs, convergence, and other factors discussed in this study. Moreover, all dependencies influencing the research results must be incorporated into the model equations in computational models based on such methods. The challenge here is that representing specific complex multiphase processes in equations is complicated and sometimes even impossible. Firstly, such processes often exhibit a nonlinear nature, significantly complicating the model and calculations. Secondly, most of these dependencies are empirical or purely intuitive, meaning there is no guarantee that they accurately describe the process in all its phases. Consequently, there is a pressing need to find a tool capable of accurately modeling such processes. The application of artificial intelligence in general, and neural networks in particular, is a novel approach in the study of rotor systems. However, it has already proven to be a powerful and promising tool for solving a wide range of problems, including modeling implicit and complex processes. This review has presented several studies [2; 13; 14; 15; 24; 25; 26; 30; 59; 86; 87; 90] where researchers have effectively used neural networks. However, issues related to this approach are already emerging. One of the most significant problems is the poor scalability of neural network algorithms. An algorithm developed for a specific system is highly likely to fail even with minor changes, such as alterations in geometric or initial parameters, let alone the addition, relocation, or combination of defects. All the aforementioned authors note this issue in their studies. Table 13 presents a brief description of methods for ANN scaling improvement. Thus, a key direction for further research could be improving the scalability of such algorithms. Several approaches can be applied to rotor system studies, among the methods considered to enhance scalability. A summary of these methods, along with their advantages and disadvantages, is presented in Table 12.  
As shown in the table, each method and its potential improvements introduce new challenges. Some of these challenges can be addressed with straightforward solutions. For instance, in the mixture-of-experts (MoE) approach, instead of using a gating neural network, a fuzzy logic clustering module could be employed. Similarly, combining a neural acceleration subsystem (NAS) with the principles of a spiking neural network (SNN) for optimizing the architecture based on certain predetermined weights selected statistically as the most significant under varying conditions could enhance its efficiency. However, more complex solutions remain the subject of future research. Figure 37: The methodological domains vs. key evaluation criteria. After a thorough and robust analysis of physical modeling methods and their comparison with AI-based models, it can be concluded that physical modeling relies on well-established equations capturing rotor mass, stiffness, damping, and gyroscopic effects to predict critical speeds and vibration modes accurately. These models are interpretable and reliable within known conditions; however, they can struggle with complex nonlinearities or uncertainties. Conversely, AI-based models use sensor data to detect patterns, predict faults, and enable real-time monitoring. They excel in handling noisy, complex data and adapting to operational changes but often lack physical interpretability and can fail outside their training scope. Overall, hybrid methods involve evaluating accuracy, generalizability, interpretability, and computational cost. Combining physical insights with AI's adaptability offers the most promising path for improved prediction, fault diagnosis, and system optimization in rotor dynamics.