Data-Driven Extreme Response Estimation

• URL: http://arxiv.org/abs/2503.21638v1
• Date: Thu, 27 Mar 2025 16:03:46 GMT
• Title: Data-Driven Extreme Response Estimation
• Authors: Samuel J. Edwards, Michael D. Levine,
• Abstract summary: A method to rapidly estimate extreme ship response events is developed in this paper.<n>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.<n>More focus is placed on larger responses by isolating the time-series near peak events and training on only the shorter time-series around the large event.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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