{"url":"/dataset/pino-geo-var-plate-stress","name":"PINO-geo_var-plate_stress","full_name":null,"description_markdown":"This dataset is well-structured for the physics-informed training of Neural operators for varying domain geometry, which provides the FEM results of solving a 2D plate stress problem in a domain geometry shape of a rectangle with four holes of different locations and sizes. The Github of the paper that first uses this dataset is: https://github.com/WeihengZ/PI-GANO.","description_withheld":null,"homepage":"","introduced_date":"2024-08-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/2408-01600","title":"Physics-Informed Geometry-Aware Neural Operator","first_author":"Weiheng Zhong","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["PINO-geo_var-plate_stress"],"data_loaders":[{"repo":"https://github.com/weihengz/pi-gano","url":"https://github.com/weihengz/pi-gano","frameworks":["pytorch"]}],"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":"2026-09-25T09:33:49+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."}