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.
@article{arxiv.2307.08810,
title = {Operator Guidance Informed by AI-Augmented Simulations},
author = {Samuel J. Edwards and Michael Levine},
journal= {arXiv preprint arXiv:2307.08810},
year = {2023}
}
Comments
Presented at the 22nd Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT) in Drubeck, Germany on May 25th, 2023