Zero-shot generalization of transformer neural operators to larger domains
Transformer-based neural operators have shown remarkable performance for approximating solution operators of partial differential equations on complex geometries. However, existing approaches implicitly assume a fixed domain size, which limits their ability to generalize at inference. In this work, we investigate domain extension, namely zero-shot inference on spatial domains that are significantly larger than those encountered during training. We argue that this setting fundamentally requires spatial locality and translation equivariance. We propose to implement this locality via a decomposable bias in the attention logits computation, enabling finely controllable locality while remainin...
| Authors | Armand de Villeroché, Sibo Cheng, Vincent Le Guen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 June 2026 |
| Last updated at source | 1 October 2026 |
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