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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/2512.00104 |
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| _version_ | 1866917113410617344 |
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| author | Mendez, Miguel A. Berghe, Jan van Den Ratz, Manuel Fiore, Matilde Schena, Lorenzo |
| author_facet | Mendez, Miguel A. Berghe, Jan van Den Ratz, Manuel Fiore, Matilde Schena, Lorenzo |
| contents | This chapter provides three tutorial exercises on physics-constrained regression. These are implemented as toy problems that seek to mimic grand challenges in (1) the super-resolution and data assimilation of the velocity field in image velocimetry, (2) data-driven turbulence modeling, and (3) system identification and digital twinning for forecasting and control. The Python codes for all exercises are provided in the course repository. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00104 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning with Physical Constraints Mendez, Miguel A. Berghe, Jan van Den Ratz, Manuel Fiore, Matilde Schena, Lorenzo Fluid Dynamics Machine Learning This chapter provides three tutorial exercises on physics-constrained regression. These are implemented as toy problems that seek to mimic grand challenges in (1) the super-resolution and data assimilation of the velocity field in image velocimetry, (2) data-driven turbulence modeling, and (3) system identification and digital twinning for forecasting and control. The Python codes for all exercises are provided in the course repository. |
| title | Learning with Physical Constraints |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2512.00104 |