Physics-informed low-rank neural operators with application to parametric elliptic PDEs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915486626742272 |
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| author | Schaffer, Sebastian Exl, Lukas |
| author_facet | Schaffer, Sebastian Exl, Lukas |
| contents | We present the Physics-Informed Low-Rank Neural Operator (PILNO), a neural operator framework for efficiently approximating solution operators of partial differential equations (PDEs) on point cloud data. PILNO combines low-rank kernel approximations with an encoder--decoder architecture, enabling fast, continuous one-shot predictions while remaining independent of specific discretizations. The model is trained using a physics-informed penalty framework, ensuring that PDE constraints and boundary conditions are satisfied in both supervised and unsupervised settings. We demonstrate its effectiveness on diverse problems, including function fitting, the Poisson equation, the screened Poisson equation with variable coefficients, and parameterized Darcy flow. The low-rank structure provides computational efficiency in high-dimensional parameter spaces, establishing PILNO as a scalable and flexible surrogate modeling tool for PDEs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_07687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed low-rank neural operators with application to parametric elliptic PDEs Schaffer, Sebastian Exl, Lukas Numerical Analysis Computational Physics Machine Learning 68T07, 65N80, 65F55, 35J05, 76S05 We present the Physics-Informed Low-Rank Neural Operator (PILNO), a neural operator framework for efficiently approximating solution operators of partial differential equations (PDEs) on point cloud data. PILNO combines low-rank kernel approximations with an encoder--decoder architecture, enabling fast, continuous one-shot predictions while remaining independent of specific discretizations. The model is trained using a physics-informed penalty framework, ensuring that PDE constraints and boundary conditions are satisfied in both supervised and unsupervised settings. We demonstrate its effectiveness on diverse problems, including function fitting, the Poisson equation, the screened Poisson equation with variable coefficients, and parameterized Darcy flow. The low-rank structure provides computational efficiency in high-dimensional parameter spaces, establishing PILNO as a scalable and flexible surrogate modeling tool for PDEs. |
| title | Physics-informed low-rank neural operators with application to parametric elliptic PDEs |
| topic | Numerical Analysis Computational Physics Machine Learning 68T07, 65N80, 65F55, 35J05, 76S05 |
| url | https://arxiv.org/abs/2509.07687 |