Data-Driven Extreme Response Estimation
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913762722709504 |
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| author | Edwards, Samuel J. Levine, Michael D. |
| author_facet | Edwards, Samuel J. Levine, Michael D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Extreme Response Estimation Edwards, Samuel J. Levine, Michael D. Machine Learning Data Analysis, Statistics and Probability 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. |
| title | Data-Driven Extreme Response Estimation |
| topic | Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2503.21638 |