采用可泛化力修正机器学习方法预测不同海况下的船舶响应
Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method
AI总结：
针对船舶响应预测中机器学习泛化性不足、高保真训练数据获取成本高的问题，提出修正低保真运动方程力的混合机器学习方法，经小数据集训练后，相比线性物理模型与LSTM模型，预测精度和泛化能力更优。
摘要：
若一种机器学习（ML）方法能对不同于训练数据集的输入做出预测，则该方法具备可泛化性。对于波浪诱导船舶响应的预测而言，若要让机器学习方法在设计评估中发挥作用，泛化能力是一项重要考量因素。此外，训练数据集的规模对方法的实用性有显著影响，尤其是当训练数据由成本高昂的高保真数值工具生成时。本文研究一种混合机器学习方法，该方法对低保真运动方程中的力进行修正。该方法被应用于两个不同的案例研究：受不规则激励的Duffing方程的非线性响应，以及快速排水型船（Fast Displacement Ship, FDS）在迎浪中的高保真垂荡与纵摇响应数据。研究通过预测不同于训练数据集的不规则波浪工况下的响应，确定了该方法在两个案例中的泛化能力，同时还探究了混合模型中基于物理的低保真项对泛化能力的影响。研究将预测结果与两个基准模型进行对比：线性物理模型和数据驱动的LSTM模型。结果发现，当在小数据集上训练时，该混合方法在预测精度和泛化能力上均有所提升。
英文摘要：
A machine learning (ML) method is generalizable if it can make predictions on inputs which differ from the training dataset. For predictions of wave-induced ship responses, generalizability is an important consideration if ML methods are to be useful in design evaluations. Furthermore, the size of the training dataset has a significant impact on the practicality of a method, especially when training data is generated using high-fidelity numerical tools which are expensive. This paper considers a hybrid machine learning method which corrects the force in a low-fidelity equation of motion. The method is applied to two different case studies: the nonlinear responses of a Duffing equation subject to irregular excitation, and high-fidelity heave and pitch response data of a Fast Displacement Ship (FDS) in head seas. The generalizability of the method is determined in both cases by making predictions of the response in irregular wave conditions that differ from those in the training dataset. The influence that low-fidelity physics-based terms in the hybrid model have on generalizability is also investigated. The predictions are compared to two benchmarks: a linear physics-based model and a data-driven LSTM model. It is found that the hybrid method offers an improvement in prediction accuracy and generalizability when trained on a small dataset.
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。
