Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer

Xiaofeng Xue; Xiaoling Gong; Jacek Mańdziuk; Jun Yao; El Sayed M. El-Alfy; Jian Wang

doi:10.1007/978-981-99-8079-6_35

Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer

Xiaofeng Xue, Xiaoling Gong, Jacek Mańdziuk, Jun Yao, El Sayed M. El-Alfy, Jian Wang^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Theory-guided neural network recently has been used to solve partial differential equations. This method has received widespread attention due to its low data requirements and adherence to physical laws during the training process. However, the selection of the punishment coefficient for including physical laws as a penalty term in the loss function undoubtedly affects the performance of the model. In this paper, we propose a comprehensive theory-guided framework using a bilevel programming model that can adaptively adjust the hyperparameters of the loss function to further enhance the performance of the model. An enhanced water flow optimizer (EWFO) algorithm is applied to optimize upper-level variables in the framework. In this algorithm, an opposition-based learning technic is used in the initialization phase to boost the initial group quality; a nonlinear convergence factor is added to the laminar flow operator to upgrade the diversity of the group and expand the search range. The experiments show that competitive performance of the method in solving stochastic partial differential equations.

Original language	English
Title of host publication	Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
Editors	Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	448-461
Number of pages	14
ISBN (Print)	9789819980789
DOIs	https://doi.org/10.1007/978-981-99-8079-6_35
State	Published - 2024
Event	30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China Duration: 20 Nov 2023 → 23 Nov 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14447 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Neural Information Processing, ICONIP 2023
Country/Territory	China
City	Changsha
Period	20/11/23 → 23/11/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Keywords

bilevel programming
penalty
stochastic partial differential equation
theory-guided neural network
water flow optimizer

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-981-99-8079-6_35

Cite this

Xue, X., Gong, X., Mańdziuk, J., Yao, J., El-Alfy, E. S. M., & Wang, J. (2024). Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer. In B. Luo, L. Cheng, Z.-G. Wu, H. Li, & C. Li (Eds.), Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings (pp. 448-461). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14447 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8079-6_35

Xue, Xiaofeng ; Gong, Xiaoling ; Mańdziuk, Jacek et al. / Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer. Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. editor / Biao Luo ; Long Cheng ; Zheng-Guang Wu ; Hongyi Li ; Chaojie Li. Springer Science and Business Media Deutschland GmbH, 2024. pp. 448-461 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Theory-guided neural network recently has been used to solve partial differential equations. This method has received widespread attention due to its low data requirements and adherence to physical laws during the training process. However, the selection of the punishment coefficient for including physical laws as a penalty term in the loss function undoubtedly affects the performance of the model. In this paper, we propose a comprehensive theory-guided framework using a bilevel programming model that can adaptively adjust the hyperparameters of the loss function to further enhance the performance of the model. An enhanced water flow optimizer (EWFO) algorithm is applied to optimize upper-level variables in the framework. In this algorithm, an opposition-based learning technic is used in the initialization phase to boost the initial group quality; a nonlinear convergence factor is added to the laminar flow operator to upgrade the diversity of the group and expand the search range. The experiments show that competitive performance of the method in solving stochastic partial differential equations.",

keywords = "bilevel programming, penalty, stochastic partial differential equation, theory-guided neural network, water flow optimizer",

author = "Xiaofeng Xue and Xiaoling Gong and Jacek Ma{\'n}dziuk and Jun Yao and El-Alfy, {El Sayed M.} and Jian Wang",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 30th International Conference on Neural Information Processing, ICONIP 2023 ; Conference date: 20-11-2023 Through 23-11-2023",

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Xue, X, Gong, X, Mańdziuk, J, Yao, J, El-Alfy, ESM & Wang, J 2024, Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer. in B Luo, L Cheng, Z-G Wu, H Li & C Li (eds), Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14447 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 448-461, 30th International Conference on Neural Information Processing, ICONIP 2023, Changsha, China, 20/11/23. https://doi.org/10.1007/978-981-99-8079-6_35

Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer. / Xue, Xiaofeng; Gong, Xiaoling; Mańdziuk, Jacek et al.
Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. ed. / Biao Luo; Long Cheng; Zheng-Guang Wu; Hongyi Li; Chaojie Li. Springer Science and Business Media Deutschland GmbH, 2024. p. 448-461 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14447 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Xue, Xiaofeng

AU - Gong, Xiaoling

AU - Mańdziuk, Jacek

AU - Yao, Jun

AU - El-Alfy, El Sayed M.

AU - Wang, Jian

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Theory-guided neural network recently has been used to solve partial differential equations. This method has received widespread attention due to its low data requirements and adherence to physical laws during the training process. However, the selection of the punishment coefficient for including physical laws as a penalty term in the loss function undoubtedly affects the performance of the model. In this paper, we propose a comprehensive theory-guided framework using a bilevel programming model that can adaptively adjust the hyperparameters of the loss function to further enhance the performance of the model. An enhanced water flow optimizer (EWFO) algorithm is applied to optimize upper-level variables in the framework. In this algorithm, an opposition-based learning technic is used in the initialization phase to boost the initial group quality; a nonlinear convergence factor is added to the laminar flow operator to upgrade the diversity of the group and expand the search range. The experiments show that competitive performance of the method in solving stochastic partial differential equations.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Xue X, Gong X, Mańdziuk J, Yao J, El-Alfy ESM, Wang J. Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer. In Luo B, Cheng L, Wu ZG, Li H, Li C, editors, Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 448-461. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-8079-6_35

Theory-Guided Convolutional Neural Network with an Enhanced Water Flow Optimizer

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this