PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914247997390848 |
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| author | Jiang, Zicong Karlsson, Magnus Agrell, Erik Häger, Christian |
| author_facet | Jiang, Zicong Karlsson, Magnus Agrell, Erik Häger, Christian |
| contents | We propose physics-informed digital twin (PIDT): a fiber parameter estimation approach that combines a parameterized split-step method with a physics-informed loss. PIDT improves accuracy and convergence speed with lower complexity compared to previous neural operators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07436 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation Jiang, Zicong Karlsson, Magnus Agrell, Erik Häger, Christian Signal Processing Machine Learning Optics We propose physics-informed digital twin (PIDT): a fiber parameter estimation approach that combines a parameterized split-step method with a physics-informed loss. PIDT improves accuracy and convergence speed with lower complexity compared to previous neural operators. |
| title | PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation |
| topic | Signal Processing Machine Learning Optics |
| url | https://arxiv.org/abs/2601.07436 |