## 3 Potential of Intelligent Algorithms for Hydrodynamic
### Machine Learning (ML)  
Machine learning aims to develop algorithms that can automatically learn and make predictions or decisions from data. Unlike deep learning, which relies heavily on multi-layer neural networks to automatically extract features from data, traditional machine learning methods (e.g., support vector machines, decision trees, and integrated learning methods) rely more on manual feature engineering and statistical inference. These algorithms achieve the prediction and classification of unknown samples by constructing models of relationships between data. One of their major advantages is that they are able to build fast and efficient data-driven models using historical experimental data or numerical simulation data, thus reducing the reliance on computationally resource-intensive numerical simulations [75]. However, the performance of these methods is highly dependent on the quality and quantity of the training data, while the generalization ability of the models and the interpretation of the intrinsic laws of physics are still challenges that need to be addressed. The application of machine learning in hydrodynamic noise research focuses on noise prediction, noise source identification, and innovative exploration of hybrid methods. Using regression modelling and other supervised learning methods, quantitative relationships between flow parameters and noise intensity can be established, enabling rapid prediction of noise levels. This data-driven modelling approach demonstrates significant advantages in real-time noise estimation, providing an efficient alternative to traditional CFD-based numerical simulations. Meanwhile, classification algorithms (e.g., support vector machines and decision trees) show strong capability in effectively distinguishing between different noise sources in complex acoustic signals, which provides a basis for the development of noise control strategies. The robustness and accuracy of the model can also be further improved by adopting integrated methods such as random forests, to cope with nonlinear, multivariate, and complex problems. In addition, some studies in recent years have attempted to integrate multiple machine learning methods to form a hybrid strategy to optimize noise prediction and control. [PERSON] [76] proposed a machine-learning-based framework for the analysis of ocean hydrodynamics, which achieved a quantitative description of the noise generation mechanism in a complex flow field by integrating a variety of data-driven methods, thus providing new ideas for noise reduction and structural optimization. Meanwhile, the study by [PERSON] et al. [77] utilized deep-reinforcement-learning-driven synthetic jet technology to achieve active control of unsteady flow noise, effectively reducing wake amplitude and noise radiation. In addition, [PERSON] et al. [78] accurately identified fluid instability through convolutional neural networks, providing real-time detection and classification of noise sources, with reliable technical support. Overall, these works demonstrate the potential of the fusion of multiple machine learning methods applied in hydrodynamic noise prediction and control, and provide powerful data-driven technical support for high-precision, real-time response, as well as intelligent control in this field. Reinforcement learning (RL) is an intelligent optimization method based on a trial-and-error mechanism and reward feedback, which learns an optimal decision-making strategy through interaction between the intelligence and the environment. Unlike traditional supervised learning, reinforcement learning does not rely on a fixed training dataset, but continuously optimizes the control strategy through the balance of exploration and exploitation, which is particularly suitable for the optimal regulation of complex dynamic systems.