Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Choose a Transformer: Fourier or Galerkin
Authors: Shuhao Cao
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present three operator learning experiments, including the viscid Burgers equation, an interface Darcy flow, and an inverse interface coefficient identification problem. The newly proposed simple attention-based operator learner, Galerkin Transformer, shows significant improvements in both training cost and evaluation accuracy over its softmax-normalized counterparts. |
| Researcher Affiliation | Academia | Shuhao Cao Department of Mathematics and Statistics Washington University in St. Louis EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The Py Torch codes to reproduce our results are available as an open-source software. 1 https://github.com/scaomath/galerkin-transformer |
| Open Datasets | Yes | The data are obtained courtesy of the PDE benchmark under the MIT license.3 https://github.com/zongyi-li/fourier_neural_operator |
| Dataset Splits | No | The data is split 80%/20% for training/evaluation for all three examples. While a train/test split is mentioned, a separate validation split is not explicitly specified. |
| Hardware Specification | Yes | The training and evaluation is done on a single GPU with 32GB of memory. Specifically, the reported benchmarks in Table 1 use an NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions several software libraries like PyTorch, NumPy, and SciPy in the acknowledgments, but does not provide specific version numbers for them as dependencies. |
| Experiment Setup | Yes | All attention-based models match the parameter quota of the baseline, and are trained using the loss in (2) with the same 1cycle scheduler [78] for 100 epochs. |