AI Collection

Conference item : Abstract

Enhancing tidal analysis and prediction using physics informed machine learning

Publication status:: Accepted

Peer review status:: Peer reviewed

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Share
Cite

Cite this record

APA Style

Monahan, T., Tang, T., Roberts, S., & Adcock, T. A. A. (2024). Enhancing tidal analysis and prediction using physics informed machine learning. Young Coastal Scientists and Engineers 2024 (YCSEC 2024).

MLA Style

Monahan, T, et al. “Enhancing Tidal Analysis and Prediction Using Physics Informed Machine Learning.” Young Coastal Scientists and Engineers 2024 (YCSEC 2024), 2024.

Chicago Style

Monahan, T, T Tang, S Roberts, and TAA Adcock. 2024. “Enhancing Tidal Analysis and Prediction Using Physics Informed Machine Learning.” In Young Coastal Scientists and Engineers 2024 (YCSEC 2024).
Print

Access Document

Files:: Monahan_et_al_2024_Enhancing_tidal_analysis.pdf

(Preview, Accepted manuscript, pdf, 91.4KB, Terms of use)

Authors

+ Monahan, T More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author

+ Tang, T More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author
ORCID:: 0000-0002-6365-9342

+ Roberts, S More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author

+ Adcock, TAA More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Engineering Science
Role:: Author
ORCID:: 0000-0001-7556-1193

Publication date:: 2024-04-03
Acceptance date:: 2024-02-14
Event title:: Young Coastal Scientists and Engineers 2024 (YCSEC 2024)
Event location:: Wallingford, UK
Event website:: https://www.hrwallingford.com/YCSEC2024
Event start date:: 2024-03-26
Event end date:: 2024-03-27

Language:: English
Keywords:: tides

scientific machine learning
Subtype:: Abstract
Pubs id:: 1920435
Local pid:: pubs:1920435
Deposit date:: 2024-03-28
ARK identifier:: ark:/29072/ora_7b3777c5d6cc4b37919432b3cba52a52

Terms of use

Copyright holder:: Monahan et al.
Copyright date:: 2024
Rights statement:: © 2024 The Authors.
Notes:: This paper was presented at the Young Coastal Scientists and Engineers 2024 (YCSEC 2024), 26-27 March 2024, Walingford, UK. This is the accepted manuscript version of the paper.

Licence:: Terms and Conditions of Use for Oxford University Research Archive

Views and Downloads

About views and downloads

If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP