Operator Guidance Informed by AI-Augmented Simulations

This paper will present a multi-fidelity, data-adaptive approach with a Long Short-Term Memory (LSTM) neural network to estimate ship response statistics in bimodal, bidirectional seas. The study will employ a fast low-fidelity, volume-based tool SimpleCode and a higher-fidelity tool known as the La...

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Bibliographic Details
Main Authors Edwards, Samuel J, Levine, Michael
Format Journal Article
LanguageEnglish
Published 17.07.2023
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Physics - Atmospheric and Oceanic Physics
Statistics - Applications
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Summary:This paper will present a multi-fidelity, data-adaptive approach with a Long Short-Term Memory (LSTM) neural network to estimate ship response statistics in bimodal, bidirectional seas. The study will employ a fast low-fidelity, volume-based tool SimpleCode and a higher-fidelity tool known as the Large Amplitude Motion Program (LAMP). SimpleCode and LAMP data were generated by common bi-modal, bi-directional sea conditions in the North Atlantic as training data. After training an LSTM network with LAMP ship motion response data, a sample route was traversed and randomly sampled historical weather was input into SimpleCode and the LSTM network, and compared against the higher fidelity results.
DOI:10.48550/arxiv.2307.08810