Inpainting physics: self-supervised learning for context-driven fluid simulation
This work addresses the limited reusability of neural surrogates for computational fluid dynamics under boundary-condition shifts or local geometry changes.
The authors reformulate steady CFD inference as an inpainting problem, learning a self-supervised prior over velocity fields and imposing boundary constraints only during inference. On intracranial aneurysm hemodynamics, their method outperforms supervised neural surrogates under boundary-condition and dataset shift, enabling local geometry editing.
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 this idea to large 3D meshes, we introduce a local neighbourhood tokeniser that represents high-resolution velocity fields as compact spatial latent tokens and train latent flow-matching and masked-autoencoder models on these tokens. On intracranial aneurysm hemodynamics, our method reconstructs full velocity fields from sparse boundary context, outperforms supervised neural surrogates under boundary-condition and dataset shift and enables local geometry editing by reusing unchanged simulation context. These results suggest that viewing CFD inference as context-conditioned inpainting can turn neural surrogates from task-specific predictors into reusable flow priors.