机器学习在船舶、海洋和海洋工程中的应用
Machine Learning for Naval Architecture, Ocean and Marine Engineering
摘要：
基于机器学习（ML）的算法已经在工程和科学的许多领域中产生了重大影响，其中数据集可以从实验和高保真数值模拟中获得。这些数据集通常用于机器学习模型中，以提取关于底层物理的信息，并导出将输入变量映射到感兴趣的目标量的函数关系。科学机器学习（SciML）中常用的机器学习算法包括神经网络、回归树、随机森林、支持向量机等。本文的重点是回顾ML在海军建筑、海洋和海洋工程问题中的应用，并确定优先研究方向。我们讨论了机器学习算法在不同问题中的应用，如波高预测、船舶风荷载计算、海上平台损伤检测、船舶附加阻力计算以及沿海和海洋环境中的各种其他应用。包括ML模型开发中使用的数据集源在内的数据集的详细信息也包括在内。详细介绍了作为ML模型输入的特征，最后讨论了ML模型的优化方法。在此全面分析的基础上，我们指出了未来的研究方向，这些方向对于ML在海洋和海洋工程问题中的应用可能会富有成效。
英文摘要：
Machine Learning (ML) based algorithms have found significant impact in many fields of engineering and sciences, where datasets are available from experiments and high fidelity numerical simulations. Those datasets are generally utilized in a machine learning model to extract information about the underlying physics and derive functional relationships mapping input variables to target quantities of interest. Commonplace machine learning algorithms utilized in Scientific Machine Learning (SciML) include neural networks, regression trees, random forests, support vector machines, etc. The focus of this article is to review the applications of ML in naval architecture, ocean, and marine engineering problems; and identify priority directions of research. We discuss the applications of machine learning algorithms for different problems such as wave height prediction, calculation of wind loads on ships, damage detection of offshore platforms, calculation of ship added resistance, and various other applications in coastal and marine environments. The details of the data sets including the source of data-sets utilized in the ML model development are included. The features used as the inputs to the ML models are presented in detail and finally, the methods employed in optimization of the ML models were also discussed. Based on this comprehensive analysis we point out future directions of research that may be fruitful for the application of ML to the ocean and marine engineering problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
