Operator Guidance Informed by AI-Augmented Simulations
Samuel J. Edwards Michael Levine
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.