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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/2507.15409 |
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| _version_ | 1866911103276023808 |
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| author | Ye, Zhanhong Liu, Zining Wu, Bingyang Jiang, Hongjie Chen, Leheng Zhang, Minyan Huang, Xiang Zou, Qinghe Meng. Jingyuan Liu, Hongsheng Dong, Bin |
| author_facet | Ye, Zhanhong Liu, Zining Wu, Bingyang Jiang, Hongjie Chen, Leheng Zhang, Minyan Huang, Xiang Zou, Qinghe Meng. Jingyuan Liu, Hongsheng Dong, Bin |
| contents | Partial differential equations (PDEs) play a central role in describing many physical phenomena. Various scientific and engineering applications demand a versatile and differentiable PDE solver that can quickly generate solutions with adequate accuracy, and limitations of the traditional solvers and specialized neural operators motivate the development of foundation models for solving PDEs. This paper introduces PDEformer-2, a versatile foundation model for two-dimensional PDEs. Based on our previous one-dimensional PDEformer-1 model, PDEformer-2 receives the PDE form as network input via computational graph representation, which has the flexibility to encode most common PDEs. The mesh-free predicted solutions can be directly queried at arbitrary spatio-temporal coordinates. A large (40TB) diverse dataset is employed to pretrain the current model, making it capable of simultaneously addressing PDEs with different symbolic forms, domain shapes, boundary conditions, number of variables, and time-dependency. Accurate zero-shot prediction is allowed for PDEs that resemble the pretraining ones. When adapted to new unseen PDEs, PDEformer-2 demonstrates faster learning than many specialized models, and has smaller errors given limited (less than 100) samples. Additionally, PDEformer-2 can be employed in the inverse problems thanks to its fast and differentiable nature and produces reasonable results in our experiments to recover coefficient scalars and fields of a PDE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PDEformer-2: A Versatile Foundation Model for Two-Dimensional Partial Differential Equations Ye, Zhanhong Liu, Zining Wu, Bingyang Jiang, Hongjie Chen, Leheng Zhang, Minyan Huang, Xiang Zou, Qinghe Meng. Jingyuan Liu, Hongsheng Dong, Bin Numerical Analysis Partial differential equations (PDEs) play a central role in describing many physical phenomena. Various scientific and engineering applications demand a versatile and differentiable PDE solver that can quickly generate solutions with adequate accuracy, and limitations of the traditional solvers and specialized neural operators motivate the development of foundation models for solving PDEs. This paper introduces PDEformer-2, a versatile foundation model for two-dimensional PDEs. Based on our previous one-dimensional PDEformer-1 model, PDEformer-2 receives the PDE form as network input via computational graph representation, which has the flexibility to encode most common PDEs. The mesh-free predicted solutions can be directly queried at arbitrary spatio-temporal coordinates. A large (40TB) diverse dataset is employed to pretrain the current model, making it capable of simultaneously addressing PDEs with different symbolic forms, domain shapes, boundary conditions, number of variables, and time-dependency. Accurate zero-shot prediction is allowed for PDEs that resemble the pretraining ones. When adapted to new unseen PDEs, PDEformer-2 demonstrates faster learning than many specialized models, and has smaller errors given limited (less than 100) samples. Additionally, PDEformer-2 can be employed in the inverse problems thanks to its fast and differentiable nature and produces reasonable results in our experiments to recover coefficient scalars and fields of a PDE. |
| title | PDEformer-2: A Versatile Foundation Model for Two-Dimensional Partial Differential Equations |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2507.15409 |