{"url":"/dataset/camels-multifield-dataset","name":"CAMELS Multifield Dataset","full_name":"CAMELS Multifield Dataset","description_markdown":"CMD is a publicly available collection of hundreds of thousands 2D maps and 3D grids containing different properties of the gas, dark matter, and stars from more than 2,000 different universes. The data has been generated from thousands of state-of-the-art (magneto-)hydrodynamic and gravity-only N-body simulations from the CAMELS project.\r\n\r\nEach 2D map and 3D grid has a set of labels associated to it: 2 cosmological parameters characterizing fundamental properties of the Universe, and 4 astrophysical parameters parametrizing the strength of astrophysical processes such as feedback from supernova and supermassive black-holes.\r\n\r\nThe main task this dataset was designed is to perform a robust inference on the value of the cosmological parameters from each map and grid. The data itself was generated from two completely different set of simulations, and it is not obvious that training one model on one will work when predicting on the other. Since simulations of the real Universe may never be perfect, this dataset provides the data to tackle this problem.\r\n\r\nSolving this problem will help cosmologists to constrain the value of the cosmological parameters with the highest accuracy and therefore unveil the mysteries of our Universe. CMD can also be used for many other tasks, such as field mapping and super-resolution.","description_withheld":null,"homepage":"https://camels-multifield-dataset.readthedocs.io","introduced_date":"2021-09-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-camels-multifield-dataset-learning-the","title":"The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence","first_author":"Francisco Villaescusa-Navarro","url":null},"license":{"name":"BY-NC-SA","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Physics","url":"/datasets/modality/physics"}],"tasks":[{"name":"3D Object Super-Resolution","url":"/task/3d-object-super-resolution","datasets_with_task":"/datasets/task/3d-object-super-resolution"},{"name":"Physical Simulations","url":"/task/physical-simulations","datasets_with_task":"/datasets/task/physical-simulations"},{"name":"Multi-target regression","url":"/task/multi-target-regression","datasets_with_task":"/datasets/task/multi-target-regression"},{"name":"Physics-informed machine learning","url":"/task/physics-informed-machine-learning","datasets_with_task":"/datasets/task/physics-informed-machine-learning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CAMELS Multifield Dataset"],"data_loaders":[{"repo":"https://github.com/franciscovillaescusa/CMD","url":"https://camels-multifield-dataset.readthedocs.io","frameworks":["pytorch"]}],"num_papers_in_archive":6,"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."}