Operator Guidance Informed by AI-Augmented Simulations

Samuel J. Edwards, Michael Levine
7/17/2023
cs.AIcs.LGphysics.ao-ph

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Abstract

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.

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Cite this paper

@article{edwards2023operator,
  title  = {Operator Guidance Informed by AI-Augmented Simulations},
  author = {Samuel J. Edwards and Michael Levine},
  year   = {2023},
  eprint = {2307.08810},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.08810v1}
}

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