Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data With Different Platform Loads
Author:
Affiliation:
1. Numerical Offshore Tank, Universidade de São Paulo (USP), São Paulo, Brazil
2. Petrobras, Rio de Janeiro, Brazil
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES Finance Code 001), Brazil
ANP/PETROBRAS, Brazil
CNPq
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09857835.pdf?arnumber=9857835
Reference17 articles.
1. Neural architecture search: A survey;elsken;arXiv 1808 05377,2018
2. A tutorial on Bayesian optimization;frazier;arXiv 1807 02811,2018
3. Algorithms for hyper-parameter optimization;bergstra;Proc 24th Int Conf Neural Inf Process Syst,2011
4. Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data
5. Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction
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1. Deep neural operators can predict the real-time response of floating offshore structures under irregular waves;Computers & Structures;2024-01
2. Design of Experiments Technique Applied to Artificial Neural Network Models for FPSO Mooring System Analysis;Journal of Marine Science and Engineering;2023-11-17
3. Machine learning in coastal bridge hydrodynamics: A state-of-the-art review;Applied Ocean Research;2023-05
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