Investigating the Use of Inductive Transfer Learning and
RNN to Quantify Extreme Event Statistics of Ship Motions
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kramer-jrkramer-nave-meche-2023-thesis.pdf
Description
Thesis PDF
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5.02 MB
Format
Adobe PDF
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256ed51d68f8a8323a571d72a7b45b74
Author(s)
Kramer, Jarod
Advisor(s)
Sapsis, Themistoklis
Rubin, Joan S.
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
Ship motion software has been a critical tool for designers to study the extreme responses of ships in irregular waves. These studies and simulations often take thousands of hours to predict and analyze the ship’s motion. Simulation results are often imperative to ensure the development of accurate operational guidance, typically in the form of plots, advising the crew on safe course and speed combinations to avoid dangerous roll and pitch motions. Two programs in use by the Navy to fill this need are the fast, lower-fidelity SimpleCode program and the slower, higher-fidelity Large Amplitude Motion Program (LAMP). Previous efforts have developed a framework to leverage machine learning through a Long Short-Term Memory (LSTM) network architecture to augment the SimpleCode program by mapping its ship motion output to the more accurate LAMP output without adding significant computational overhead. This process of using an LSTM neural network to improve the SimpleCode output provides the opportunity to supply predictions and guidance to the crew in real-time. However, the limits of this mapping across various sea domains still need to be discovered. By investigating these limits, a more generalized LSTM can be realized through inductive transfer learning and a model agnostic meta-learning approach, one that leverages the training of previous networks to augment SimpleCode across a broader range of seas or produce more accurate results on a narrow set of sea conditions after very few training samples.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
System Design and Management Program.
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