Application of machine learning for the generation of tailored wave sequences

• Main Authors: Klein, Marco, Stender, Merten, Wedler, Mathies, Ehlers, Svenja, Hartmann, Moritz Cornelius Nikolaus, Desmars, Nicolas, Pick, Marc-André, Seifried, Robert, Hoffmann, Norbert
• Format: Conference Object
• Language: English
• Published: 2022
• Subjects: Arctic
• Online Access: http://hdl.handle.net/11420/13971

This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective wave sequence at the wave board. The synthetic training and validation data were acquired by applying the high-order spectral (HOS) method. The HOS method is a very accurate method for modeling non-linear wave propagation and its numerical efficiency allows the generation of large synthetic data sets. The training data featured wave groups of short duration based on JONSWAP spectra. The sea state parameters wave steepness, wave period and enhancement factor were systematically varied. At the end of the training process, the trained models were able to predict the wave sequences at the wave board based on the time series of the target wave defined for a specific target location in the wave tank. The accuracy of the trained models were evaluated by means of unseen validation data. In addition, the predictive accuracy of the trained models was compared with the classical linear transformation approach.