This article is a correction to:
Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport
Feng Qin
Hu HuangA Corrigendum on
Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport
by Qin F, Yan Z, Yang P, Tang S and Huang H (2022). Front. Energy Res. 10:979168.doi: 10.3389/fenrg.2022.979168
In the published article, there was an error in the Author list, the author list should be corrected as follows.
“Feng Qin, Zhenghe Yan, Peng Yang, Shenglai Tang and Hu Huang*”
In the published article, there was an error in the Affiliation.
The correct affiliation should be “Research Institute, CNOOC Ltd.-SHENZHEN, Shenzhan, Guangdong, China”
In the published article, there was an error in the Copyright statement. The statement should be corrected as follows.
“[Copyright © [2022] Qin, Yan, Yang, Tang and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.”
In the published article, there was an error in the Author Contributions statement. The statement should be corrected as follows.
“All authors contributed to the article and approved the submitted version.”
In the published article, there was an error in the Conflict of Interest statement. The statement should be corrected as follows.
“Authors FQ, ZY, PY, ST and HH were employed by the company CNOOC Ltd. -SHENZHEN.”
The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: deep learning, multilayer perceptron, surrogate model, pipeline simulation, dynamic weights
Citation: Qin F, Yan Z, Yang P, Tang S and Huang H (2022) Corrigendum: Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport. Front. Energy Res. 10:1109184. doi: 10.3389/fenrg.2022.1109184
Received: 27 November 2022; Accepted: 01 December 2022;
Published: 09 December 2022.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2022 Qin, Yan, Yang, Tang and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hu Huang, 303113926@qq.com