{"url":"/dataset/cylinderflow","name":"CylinderFlow","full_name":"CylinderFlow with dynamic grid","description_markdown":"This dataset captures incompressible fluid dynamics around a 2D circular cylinder within a channel:\r\n\\begin{align}\r\n    \\nabla \\cdot \\mathbf{u} &= 0, \\\\\r\n    \\partial_t \\mathbf{u} + (\\mathbf{u} \\cdot \\nabla) \\mathbf{u} &= \\nu \\nabla^2 \\mathbf{u} - \\frac{1}{\\rho} \\nabla p,\r\n\\end{align}\r\nwith boundary conditions set for velocity and pressure. It features 100 snapshots per case, with 7600 training, 1000 validation, and 1000 test samples.","description_withheld":null,"homepage":"https://github.com/lizhihao2022/AMG","introduced_date":"2024-11-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/harnessing-scale-and-physics-a-multi-graph","title":"Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries","first_author":"Zhihao LI","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["CylinderFlow"],"data_loaders":[],"num_papers_in_archive":2,"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."}