We lifted 8 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| zhijie-group/orthogonal-neural-operator | canonical | 8 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| apply_2d_rotary_pos_emb | Ran | zhijie-group/orthogonal-neural-operator/ONOmodel2.py code served (permissive licence) · get_code("6f4322bdb0cd6491") |
| apply_rotary_pos_emb | Ran | zhijie-group/orthogonal-neural-operator/ONOmodel2.py code served (permissive licence) · get_code("b9007c9564c0612a") |
| central_diff | Ran | zhijie-group/orthogonal-neural-operator/Darcy_example.py code served (permissive licence) · get_code("fbbefdd8c6f1550c") |
| central_diff | Ran | zhijie-group/orthogonal-neural-operator/time_gen.py code served (permissive licence) · get_code("44cb0b9d7d877495") |
| count_parameters | Ran | zhijie-group/orthogonal-neural-operator/Darcy_example.py code served (permissive licence) · get_code("54097d41b039f37b") |
| random_collate_fn | Ran | zhijie-group/orthogonal-neural-operator/NS_example2.py code served (permissive licence) · get_code("7a755f3c02e8f45f") |
| random_collate_fn | Ran | zhijie-group/orthogonal-neural-operator/pla_example.py code served (permissive licence) · get_code("532198a3d341e4b3") |
| rotate_half | Ran | zhijie-group/orthogonal-neural-operator/ONOmodel2.py code served (permissive licence) · get_code("8549b7478752bf18") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, attention-based neural operators have become one of the mainstreams in related research. However, existing approaches overfit the limited training data due to the considerable number of parameters in the attention mechanism. To address this, we develop an orthogonal attention based on the eigendecomposition of the kernel integral operator and the neural approximation of eigenfunctions. The orthogonalization naturally poses a proper regularization effect on the resulting neural operator, which aids in resisting overfitting and boosting generalization. Experiments on six standard neural operator benchmark datasets comprising both regular and irregular geometries show that our method can outperform competing baselines with decent margins.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.12487")
get_code_for_paper("2310.12487")
have("2310.12487")
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