Accurate prediction of main engine power is essential for vessel performance optimization, fuel efficiency, and compliance with emission regulations. Conventional machine learning approaches, such as Support Vector Machines, variants of Artificial Neural Networks (ANNs), and tree-based methods like Random Forests, Extra Tree Regressors, and XGBoost, can capture nonlinearities but often struggle to respect the fundamental propeller law relationship between power and speed, resulting in poor extrapolation outside the training envelope. This study introduces a hybrid modeling framework that integrates physics-based knowledge from sea trials with data-driven residual learning. The baseline component, derived from calm-water power curves of the form $P = cV^n$, captures the dominant power-speed dependence, while another, nonlinear, regressor is then trained to predict the residual power, representing deviations caused by environmental and operational conditions. By constraining the machine learning task to residual corrections, the hybrid model simplifies learning, improves generalization, and ensures consistency with the underlying physics. In this study, an XGBoost, a simple Neural Network, and a Physics-Informed Neural Network (PINN) coupled with the baseline component were compared to identical models without the baseline component. Validation on in-service data demonstrates that the hybrid model consistently outperformed a pure data-driven baseline in sparse data regions while maintaining similar performance in populated ones. The proposed framework provides a practical and computationally efficient tool for vessel performance monitoring, with applications in weather routing, trim optimization, and energy efficiency planning.
A comparative study of NeuralODE and Universal ODE approaches to solving Chandrasekhar White Dwarf equation
National Autonomous University of Mexico（墨西哥国立自治大学）；Vizuara AI Labs（Vizuara AI实验室）；Massachusetts Institute of Technology（麻省理工学院）；Purdue University（普渡大学）
Scientific Machine Learning with Kolmogorov-Arnold Networks
Energy \& Intelligence Lab, Department of Chemical Engineering, University of Utah, Salt Lake City, Utah 84112, USA；Department of Mechanical Engineering, School of Engineering, Urmia University of Technology, Urmia, Iran
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
Center for Frontier AI Research（前沿人工智能研究中心）；Agency for Science, Technology, and Research（新加坡科技研究局）；Institute for High Performance Computing（高性能计算研究所）；Nanyang Technological University（南洋理工大学）；Singapore Institute of Manufacturing Technology（新加坡制造技术研究所）
A Comparative Evaluation of Machine Learning Algorithms for the Prediction of R/C Buildings' Seismic Damage
Hellenic Open University（希腊开放大学）；Democritus University of Thrace（色雷斯德谟克利特大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Earthquake Planning and Protection Organization (EPPO-ITSAK)（地震规划与保护组织（EPPO-ITSAK））
