Deep neural operators can predict the real-time response of floating offshore structures under irregular waves-Reference-Cited by-同舟云学术

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Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

Published:2024-01 Issue: Volume:291 Page:107228
ISSN:0045-7949
Container-title:Computers & Structures
language:en
Short-container-title:Computers & Structures

Author:

Cao Qianying^ORCID,Goswami Somdatta,Tripura Tapas,Chakraborty Souvik,Karniadakis George Em

Funder

US Department of the Air Force

U.S. Department of Energy

Dalian University of Technology

Air Force Office of Scientific Research

Office of the Secretary of Defense

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,General Materials Science,Modeling and Simulation,Civil and Structural Engineering

Reference44 articles.

1. Time-series forecasting of ships maneuvering in waves via dynamic mode decomposition;Diez;J Ocean Eng Mar Energy,2022

2. Sparse identification of nonlinear dynamics with low-dimensionalized flow representations;Fukami;J Fluid Mech,2021

3. A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials;Goswami;Comput Methods Appl Mech Eng,2022

4. Predicting heave and surge motions of a semi-submersible with neural networks;Guo;Appl Ocean Res,2021

5. An EMD-LSTM-SVR model for the short-term roll and sway predictions of semi-submersible;Ye;Ocean Eng,2022

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effects of various freak waves on dynamic responses of a Spar-buoy floating offshore wind turbine;Ocean Engineering;2024-11

2. Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics;Neural Networks;2024-09

3. An enhanced model based on deep operator network for very short-term forecasting of ship motion;Physics of Fluids;2024-08-01

4. Laplace neural operator for solving differential equations;Nature Machine Intelligence;2024-06-24

5. Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems;Nature Communications;2024-06-14

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