FEALPy: A Cross-platform Intelligent Numerical Simulation Engine
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917323962580992 |
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| author | Zheng, Yangyang Wei, Huayi Huang, Yunqing Chen, Chunyu Tian, Tian Liu, Hanbin Wang, Wenbin He, Liang |
| author_facet | Zheng, Yangyang Wei, Huayi Huang, Yunqing Chen, Chunyu Tian, Tian Liu, Hanbin Wang, Wenbin He, Liang |
| contents | In resent years, the software ecosystem for numerical simulation still remains fragmented, with different algorithms and discretization methods often implemented in isolation, each with distinct data structures and programming conventions. This fragmentation is compounded by the growing divide between packages from different research fields and the lack of a unified, universal data structure, hindering the development of integrated, cross-platform solutions. In this work, we introduce FEALPy, a numerical simulation engine built around a unified tensor abstraction layer in a modular design. It enables seamless integration between diverse numerical methods along with deep learning workflows. By supporting multiple computational backends such as NumPy, PyTorch, and JAX, FEALPy ensures consistent adaptability across CPU and GPU hardware systems. Its modular architecture facilitates the entire simulation pipeline, from mesh handling and assembly to solver execution, with built-in support for automatic differentiation. In this paper, the versatility and efficacy of the framework are demonstrated through applications spanning linear elasticity, high-order PDEs, moving mesh methods, inverse problems and path planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06632 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FEALPy: A Cross-platform Intelligent Numerical Simulation Engine Zheng, Yangyang Wei, Huayi Huang, Yunqing Chen, Chunyu Tian, Tian Liu, Hanbin Wang, Wenbin He, Liang Numerical Analysis 65N30, 65N50, 65Y15, 65Z05, 65M60 In resent years, the software ecosystem for numerical simulation still remains fragmented, with different algorithms and discretization methods often implemented in isolation, each with distinct data structures and programming conventions. This fragmentation is compounded by the growing divide between packages from different research fields and the lack of a unified, universal data structure, hindering the development of integrated, cross-platform solutions. In this work, we introduce FEALPy, a numerical simulation engine built around a unified tensor abstraction layer in a modular design. It enables seamless integration between diverse numerical methods along with deep learning workflows. By supporting multiple computational backends such as NumPy, PyTorch, and JAX, FEALPy ensures consistent adaptability across CPU and GPU hardware systems. Its modular architecture facilitates the entire simulation pipeline, from mesh handling and assembly to solver execution, with built-in support for automatic differentiation. In this paper, the versatility and efficacy of the framework are demonstrated through applications spanning linear elasticity, high-order PDEs, moving mesh methods, inverse problems and path planning. |
| title | FEALPy: A Cross-platform Intelligent Numerical Simulation Engine |
| topic | Numerical Analysis 65N30, 65N50, 65Y15, 65Z05, 65M60 |
| url | https://arxiv.org/abs/2512.06632 |