Operator Guidance Informed by AI-Augmented Simulations
This provides a practical solution for maritime operators to improve route planning and safety in challenging ocean environments, though it is incremental in combining existing tools.
The paper tackles the problem of estimating ship response statistics in complex sea conditions by developing a multi-fidelity, data-adaptive approach using an LSTM neural network, achieving results comparable to higher-fidelity simulations with faster computation.
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.