Datasets › An operator learning perspective on parameter-to-observable maps
An operator learning perspective on parameter-to-observable maps
This repository contains the datasets corresponding to the three benchmark problems for the Fourier Neural Mappings scientific machine learning architectures. The first file is the data for the advection-diffusion problem, the second for the airfoil problem, and the third for the elliptic homogenization materials problem.
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 1 paper for it but never published that list.
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
No task tagged in the archive.
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
No modality tagged.
Languages archive 2025-07-28
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Variants archive 2025-07-28
- An operator learning perspective on parameter-to-observable maps
1 variant name, as the archive lists them.
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