Geometry-aware Incremental Neural Operator for Long-Horizon PDE prediction
Neural operators have shown strong potential for learning solution operators of partial differential equations (PDEs). However, long-horizon autoregressive prediction remains challenging: local errors accumulate as spectral inconsistency, phase misalignment, or mean drift. Existing methods mainly improve state representations and operator backbones, while leaving the repeatedly applied latent transition increment weakly structured, allowing spectral errors and unstable channel couplings to accumulate during rollout. To address these issues, we propose a geometry-aware incremental neural operator (GeoIncNO) for stable long-horizon PDE prediction. GeoIncNO predicts latent increments for res...
| Authors | Jiaquan Zhang, Shuxu Chen, Haifan Meng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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