Application of Machine Learning for Prediction of Wave-induced Ship Motion

This paper investigates the possibility of the machine learning technique being applicable in the real-time prediction of wave-induced ship motions. An integrated machine learning model is proposed by considering the two different physical attributes in the equation of motion: memory effects of past motion history and excitation forces induced by incident waves. A long short-term memory layer and a single fully connected layer were combined to establish this machine learning model. A database was constructed through the impulse response function-based numerical simulations for various ocean environments. After training, short-term deterministic predictions were conducted for new environments, and the effects of ship motion records were investigated. The response amplitude operators were evaluated based on regular wave simulations. The machine learning model was observed to have successfully learned the seakeeping characteristics of a ship.