Inpainting physics: self-supervised learning for context-driven fluid simulation
Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such as geometry and boundary conditions, to solution fields. This ties the model to the conditioning variables seen during training and limits reuse under boundary-condition shifts or local geometry changes. We propose to reformulate steady CFD inference as an inpainting problem: instead of training on explicit boundary conditions, we learn a self-supervised prior over velocity fields and impose boundary constraints only during inference by fixing known regions such as inlet, outlet or unchanged regions from previous simulations. To scale thi...
| Authors | Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert et al. |
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| Primary category | cs.LG |
| Published | 9 May 2026 |
| Last updated at source | 14 August 2026 |
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