用LSTM模型从波浪-船体数据中学习滚转失稳和统计特性变化，预测船舶在恶劣海况下的极端响应。
Learning Response-Statistic Shifts and Parametric Roll Episodes from Wave--Vessel Time Series via LSTM Functional Models
- 基于堆叠LSTM构建波浪到船体运动的非线性因果映射模型
- 成功复现参数共振滚转事件及滚转概率分布的显著偏移
- 适用于实验或仿真数据，适合船舶安全与风险评估研究者
参数共振滚转是一种罕见但高后果的船舶不稳定性，可引发滚转响应的剧烈统计变化和尾部风险。本文提出一种数据驱动的代理模型，学习从入射波浪-运动时间序列到船舶运动的非线性、因果函数映射，并验证其能准确重现（i）参数共振滚转事件，以及（ii）伴随的响应统计特性变化。该学习框架不依赖特定数据源：当船体存在时，可使用拖曳水池或水池试验中的波浪探测与运动追踪数据；设计阶段无实验条件时，可用高保真数值模拟数据。为实现严苛海况演示，我们利用改进的Pierson-Moskowitz谱生成长波不规则海况，通过URANS数值波浪水池生成训练数据，共3个海况下各49个随机相位实现实例，在固定航速下模拟出易发生参数共振的遭遇条件。采用堆叠LSTM代理模型，以波浪高度时间序列为输入，通过时域精度和分布保真度指标评估，最严重海况下模型成功捕捉了大振幅滚转的起始与增长过程，以及对应的滚转概率密度函数（PDF）变化。进一步对比均方误差、相对熵及幅度加权损失函数，揭示其在平均误差与尾部保真度之间的权衡，对操作性与风险评估具有重要意义。
原文摘要 · Abstract (English)
Parametric roll is a rare but high-consequence instability that can trigger abrupt regime changes in ship response, including pronounced shifts in roll statistics and tail risk. This paper develops a data-driven surrogate that learns the nonlinear, causal functional mapping from incident wave--motion time series to vessel motions, and demonstrates that the surrogate reproduces both (i) parametric roll episodes and (ii) the associated statistical shifts in the response. Crucially, the learning framework is data-source agnostic: the paired wave--motion time series can be obtained from controlled experiments (e.g., towing-tank or basin tests with wave probes and motion tracking) when a hull exists, or from high-fidelity simulations during design when experiments are not yet available. To provide a controlled severe-sea demonstration, we generate training data with a URANS numerical wave tank, using long-crested irregular seas synthesized from a modified Pierson--Moskowitz spectrum. The demonstration dataset comprises 49 random-phase realizations for each of three sea states, simulated at a fixed forward speed selected to yield encounter conditions under which parametric-roll episodes can occur. A stacked LSTM surrogate is trained on wave-elevation time series and evaluated on held-out realizations using time-domain accuracy and distributional fidelity metrics. In the most severe case, the model tracks the onset and growth of large-amplitude roll consistent with parametric excitation, and captures the corresponding changes in roll probability density functions (PDFs). We further compare loss-function choices (MSE, relative-entropy-based objectives, and amplitude-weighted variants) and show how they trade average error for improved tail fidelity relevant to operability and risk assessment.
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