{"url":"/dataset/an-operator-learning-perspective-on-parameter","name":"An operator learning perspective on parameter-to-observable maps","full_name":null,"description_markdown":"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.","description_withheld":null,"homepage":"https://doi.org/10.22002/r5ga1-55d06","introduced_date":"2024-02-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/an-operator-learning-perspective-on-parameter","title":"An operator learning perspective on parameter-to-observable maps","first_author":"Daniel Zhengyu Huang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["An operator learning perspective on parameter-to-observable maps"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}