用LSTM提升低精度模型对船舶极端响应的预测速度与精度。
Data-Driven Extreme Response Estimation
- 用LSTM修正低精度模型，专注峰值附近短时序列训练。
- 在海况5下预测俯仰最大值，误差显著低于普通LSTM方法。
- 适合需要快速评估极端海况下船舶响应的工程应用。
本文提出一种快速估算船舶极端响应事件的方法。通过长短期记忆网络（LSTM）对低精度水动力模型进行校正，使其达到高精度模拟水平。重点聚焦于大响应事件，仅使用低精度仿真中识别出的峰值附近短时序列进行训练。该方法在海况5（有效波高4.0米，主周期15.0秒）下，针对SimpleCode低精度求解器和大型振幅运动程序（LAMP）高精度工具的俯仰时序最大值进行测试，并与未特别关注大事件的LSTM模型进行对比。结果显示，该方法能更准确捕捉极端响应特征。
原文摘要 · Abstract (English)
A method to rapidly estimate extreme ship response events is developed in this paper. The method involves training by a Long Short-Term Memory (LSTM) neural network to correct a lower-fidelity hydrodynamic model to the level of a higher-fidelity simulation. More focus is placed on larger responses by isolating the time-series near peak events identified in the lower-fidelity simulations and training on only the shorter time-series around the large event. The method is tested on the estimation of pitch time-series maxima in Sea State 5 (significant wave height of 4.0 meters and modal period of 15.0 seconds,) generated by a lower-fidelity hydrodynamic solver known as SimpleCode and a higher-fidelity tool known as the Large Amplitude Motion Program (LAMP). The results are also compared with an LSTM trained without special considerations for large events.
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