Physics-informed Partitioned Coupled Neural Operator for Complex Networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916824672632832 |
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| author | Wu, Weidong Zhang, Yong Hao, Lili Chen, Yang Sun, Xiaoyan Gong, Dunwei |
| author_facet | Wu, Weidong Zhang, Yong Hao, Lili Chen, Yang Sun, Xiaoyan Gong, Dunwei |
| contents | Physics-Informed Neural Operators provide efficient, high-fidelity simulations for systems governed by partial differential equations (PDEs). However, most existing studies focus only on multi-scale, multi-physics systems within a single spatial region, neglecting the case with multiple interconnected sub-regions, such as gas and thermal systems. To address this, this paper proposes a Physics-Informed Partitioned Coupled Neural Operator (PCNO) to enhance the simulation performance of such networks. Compared to the existing Fourier Neural Operator (FNO), this method designs a joint convolution operator within the Fourier layer, enabling global integration capturing all sub-regions. Additionally, grid alignment layers are introduced outside the Fourier layer to help the joint convolution operator accurately learn the coupling relationship between sub-regions in the frequency domain. Experiments on gas networks demonstrate that the proposed operator not only accurately simulates complex systems but also shows good generalization and low model complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21025 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-informed Partitioned Coupled Neural Operator for Complex Networks Wu, Weidong Zhang, Yong Hao, Lili Chen, Yang Sun, Xiaoyan Gong, Dunwei Machine Learning Computational Engineering, Finance, and Science Computational Physics Physics-Informed Neural Operators provide efficient, high-fidelity simulations for systems governed by partial differential equations (PDEs). However, most existing studies focus only on multi-scale, multi-physics systems within a single spatial region, neglecting the case with multiple interconnected sub-regions, such as gas and thermal systems. To address this, this paper proposes a Physics-Informed Partitioned Coupled Neural Operator (PCNO) to enhance the simulation performance of such networks. Compared to the existing Fourier Neural Operator (FNO), this method designs a joint convolution operator within the Fourier layer, enabling global integration capturing all sub-regions. Additionally, grid alignment layers are introduced outside the Fourier layer to help the joint convolution operator accurately learn the coupling relationship between sub-regions in the frequency domain. Experiments on gas networks demonstrate that the proposed operator not only accurately simulates complex systems but also shows good generalization and low model complexity. |
| title | Physics-informed Partitioned Coupled Neural Operator for Complex Networks |
| topic | Machine Learning Computational Engineering, Finance, and Science Computational Physics |
| url | https://arxiv.org/abs/2410.21025 |