TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publication References
  4. Application of machine learning for the generation of tailored wave sequences
 
Options

Application of machine learning for the generation of tailored wave sequences

Publikationstyp
Conference Paper
Date Issued
2022-06
Sprache
English
Author(s)
Klein, Marco  orcid-logo
Stender, Merten  orcid-logo
Wedler, Mathies  orcid-logo
Ehlers, Svenja  
Hartmann, Moritz Cornelius Nikolaus  orcid-logo
Desmars, Nicolas  
Pick, Marc-AndrĂ©  
Seifried, Robert  orcid-logo
Hoffmann, Norbert  orcid-logo
Institut
Strukturdynamik M-14  
TORE-URI
http://hdl.handle.net/11420/13971
Volume
5-B
Article Number
V05BT12A015
Citation
41st International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2022)
Contribution to Conference
41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022  
Publisher DOI
10.1115/OMAE2022-78601
Scopus ID
2-s2.0-85140774383
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.
Subjects
MLE@TUHH
TUHH
WeiterfĂ¼hrende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback