Physics-Informed Neural Networks with skip connections for modeling and control of gas-lifted oil wells-Reference-Cited by-同舟云学术

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Physics-Informed Neural Networks with skip connections for modeling and control of gas-lifted oil wells

Published:2024-06 Issue: Volume:158 Page:111603
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Kittelsen Jonas Ekeland,Antonelo Eric Aislan^ORCID,Camponogara Eduardo^ORCID,Imsland Lars Struen

Funder

CNPq

CAPES

Publisher

Elsevier BV

Reference31 articles.

1. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J. Comput. Phys.,2019

2. Reconstruction of hydrofoil cavitation flow based on the chain-style physics-informed neural network;Ouyang;Eng. Appl. Artif. Intell.,2023

3. Physics-informed neural networks for modeling water flows in a river channel;Nazari;IEEE Trans. Artif. Netw.,2023

4. Navier–stokes generative adversarial network: A physics-informed deep learning model for fluid flow generation;Wu;Neural Comput. Appl.,2022

5. Physics informed extreme learning machine (PIELM)–a rapid method for the numerical solution of partial differential equations;Dwivedi;Neurocomputing,2020

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