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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01117 |
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| _version_ | 1866911033200738304 |
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| author | Sakovich, Nikita Aksenov, Dmitry Pleshakova, Ekaterina Gataullin, Sergey |
| author_facet | Sakovich, Nikita Aksenov, Dmitry Pleshakova, Ekaterina Gataullin, Sergey |
| contents | The scientific computation methods development in conjunction with artificial intelligence technologies remains a hot research topic. Finding a balance between lightweight and accurate computations is a solid foundation for this direction. The study presents a neural operator based on the dynamic mode decomposition algorithm (DMD), mapping functional spaces, which combines DMD and deep learning (DL) for spatiotemporal processes efficient modeling. Solving PDEs for various initial and boundary conditions requires significant computational resources. The method suggested automatically extracts key modes and system dynamics using them to construct predictions, reducing computational costs compared to traditional numerical methods. The approach has demonstrated its efficiency through comparative analysis of performance with closest analogues DeepONet and FNO in the heat equation, Laplaces equation, and Burgers equation solutions approximation, where it achieves high reconstruction accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Neural Operator based on Dynamic Mode Decomposition Sakovich, Nikita Aksenov, Dmitry Pleshakova, Ekaterina Gataullin, Sergey Machine Learning 68T07, 35A99 The scientific computation methods development in conjunction with artificial intelligence technologies remains a hot research topic. Finding a balance between lightweight and accurate computations is a solid foundation for this direction. The study presents a neural operator based on the dynamic mode decomposition algorithm (DMD), mapping functional spaces, which combines DMD and deep learning (DL) for spatiotemporal processes efficient modeling. Solving PDEs for various initial and boundary conditions requires significant computational resources. The method suggested automatically extracts key modes and system dynamics using them to construct predictions, reducing computational costs compared to traditional numerical methods. The approach has demonstrated its efficiency through comparative analysis of performance with closest analogues DeepONet and FNO in the heat equation, Laplaces equation, and Burgers equation solutions approximation, where it achieves high reconstruction accuracy. |
| title | A Neural Operator based on Dynamic Mode Decomposition |
| topic | Machine Learning 68T07, 35A99 |
| url | https://arxiv.org/abs/2507.01117 |