{"url":"/dataset/deepnetbeam","name":"DeepNetBeam","full_name":null,"description_markdown":"The code and database provided in this repository are related to the paper \"DeepNetBeam: A Framework for the Analysis of Functionally Graded Porous Beams\", which explores the application of various machine-learning techniques for the analysis of functionally graded porous beams. The three approaches (PINN, DEM, Neural Operator) are implemented to allow flexibility and extension for future use.\r\n\r\nThe datasets cover the required database for sections 2 and 3.\r\n\r\nYou can also generate the datasets using the provided scripts.","description_withheld":null,"homepage":"https://seafile.cloud.uni-hannover.de/d/299afa7ad11545cb9a01/","introduced_date":"2024-08-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/2408-02698","title":"Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams","first_author":"Mohammad Sadegh Eshaghi","url":null},"license":{"name":"CC BY","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["DeepNetBeam"],"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."}