An enhanced model based on deep operator network for very short-term forecasting of ship motion

被引:4
作者
Zhao, Jinxiu [1 ]
Zhao, Yong [1 ,2 ]
机构
[1] Dalian Maritime Univ, Sch Naval Architecture & Ocean Engn, Dalian 116026, Peoples R China
[2] Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China
基金
中国国家自然科学基金;
关键词
PREDICTION; DECOMPOSITION;
D O I
10.1063/5.0218666
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
Very short-term forecasting of ship motion involves forecasting future ship movements based on learned characteristics from historical motion data. However, ship motion exhibits not only temporal features but also features in the frequency domain, which are often overlooked. This paper introduces a novel method called Fourier Transform and Multilayer Perceptron-net enhancement based on Deep Operator Network (DeepONet), abbreviated as FMD. This approach effectively captures and learns ship motion patterns in both the temporal and frequency domains. Specifically, the branch net of DeepONet learns temporal features, while the trunk net performs Fourier filtering to capture the underlying ship motion patterns. In addition, the learning effectiveness of Fourier filtering is complemented by using MPL-net to enhance the extraction of detailed features in motion data. To evaluate the prediction performance of FMD, this study explores the optimal filtering frequency of the FMD model using experimental ship model motion data. Comparative testing with the DeepONet model includes multi-step prediction, coupled data forecasting, and generalization studies. Testing results demonstrate that the proposed FMD model improves prediction accuracy from 11.78% to 33.10% under Mean Squared Error (MSE) compared to the DeepONet model. Even under sea conditions ranging from mild to intense, the FMD model maintains high accuracy, with an improvement of over 30% in accuracy compared to DeepONet at longer step lengths under MSE conditions. Testing results indicate the superiority and advancement of FMD in prediction accuracy, generalization, and versatility, showcasing significant advantages in very short-term forecasting of ship motion.
引用
收藏
页数:18
相关论文
共 38 条
  • [1] Data-driven modeling of unsteady flow based on deep operator network
    Bai, Heming
    Wang, Zhicheng
    Chu, Xuesen
    Deng, Jian
    Bian, Xin
    [J]. PHYSICS OF FLUIDS, 2024, 36 (06)
  • [2] Broome D., 1990, All Days, DOI DOI 10.4043/6222-MS.OTC-6222-MS
  • [3] Deep neural operators can predict the real-time response of floating offshore structures under irregular waves
    Cao, Qianying
    Goswami, Somdatta
    Tripura, Tapas
    Chakraborty, Souvik
    Karniadakis, George Em
    [J]. COMPUTERS & STRUCTURES, 2024, 291
  • [4] COMPACT EMPIRICAL MODE DECOMPOSITION: AN ALGORITHM TO REDUCE MODE MIXING, END EFFECT, AND DETREND UNCERTAINTY
    Chu, Peter C.
    Fan, Chenwu
    Huang, Norden
    [J]. ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS, 2012, 4 (03)
  • [5] Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time
    Deka, Paresh Chandra
    Prahlada, R.
    [J]. OCEAN ENGINEERING, 2012, 43 : 32 - 42
  • [6] Neural operator prediction of linear instability waves in high-speed boundary layers
    Di Leoni, Patricio Clark
    Lu, Lu
    Meneveau, Charles
    Karniadakis, George Em
    Zaki, Tamer A.
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2023, 474
  • [7] Fleck J. T., 1965, P 1 C SHIP WAV
  • [8] An encoder-decoder architecture with Fourier attention for chaotic time series multi-step prediction
    Fu, Ke
    Li, He
    Shi, Xiaotian
    [J]. APPLIED SOFT COMPUTING, 2024, 156
  • [9] Orthogonal grid physics-informed neural networks: A neural network-based simulation tool for advection-diffusion-reaction problems
    Hou, Qingzhi
    Sun, Zewei
    He, Li
    Karemat, Alireza
    [J]. PHYSICS OF FLUIDS, 2022, 34 (07)
  • [10] [Huang Limin 黄礼敏], 2014, [船舶力学, Journal of Ship Mechanics], V18, P1534