2026-10-14T06:02:45Z https://www.scidb.cn/oai

10.57760/sciencedb.45976 2026-08-13T15:25:19Z

2026-08-13 Multiverse Parameter Dataset: 2000 Self-Consistent Universes Generated by Neural-Guided Symbolic Regression doi:10.57760/sciencedb.45976 en This dataset contains the physical constant parameters of 2,000 self-consistent universes generated through a neural-guided symbolic regression pipeline. The dataset supports the findings presented in the paper "Autonomous Discovery of Multiverse-Universal Power-Law Constraints via Neural-Guided Symbolic Regression".Data Generation Method:Candidate universes were generated via Monte Carlo sampling with parameter ranges spanning up to 6 orders of magnitude. Each candidate universe was subjected to an 8-layer physical self-consistency filter, including flatness, cosmic age, Big Bang nucleosynthesis, structure formation, causality, no-ghost condition, quantum stability, and user-defined constraints. A total of 2,000 candidate universes passed all eight layers and were retained in the final dataset.Data Content:Each universe is represented by a set of 37 physical constants, including but not limited to: the gravitational constant GG, the speed of light cc, the reduced Planck constant ℏℏ, the Boltzmann constant kBkB​, the Hubble constant H0H0​, the dark energy density ΩΛΩΛ​, the string coupling gsgs​, its dual g~sg~​s​, the string mass MsMs​, the string length ℓsℓs​, the compactification radius RR, the dual radius R~R~, the holographic dimension NN, the 10-dimensional gravitational constant G10G10​, and the cosmological constant ΛΛ. The dataset also provides the coefficient matrix for 66 power-law relations discovered via symbolic regression, as well as the detailed pass/fail status for each of the 8 filtering layers.Data Format:All data files are provided in CSV format (Unicode text, .csv), which can be accessed using standard data processing software such as Excel, Python Pandas, R, or MATLAB.Reuse Potential:This dataset can be used to validate power-law relations among physical constants, test the effectiveness of the self-consistency filtering criteria, and explore the structural characteristics of the multiverse parameter space. physical constants; multiverse; power-law relations; symbolic regression; cosmic self-consistency; cosmology; computational physics yexiwen PUBLIC https://creativecommons.org/licenses/by-sa/4.0/ dataset Science Data Bank