CGMM Parameters for Uncertainty Quantification from Physics-assisted data-driven stochastic reduced-order models for attribution of heterogeneous stress distributions in low-grain polycrystals
收藏The Royal Society Figshare2025-02-19 更新2026-04-17 收录
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https://rs.figshare.com/articles/dataset/CGMM_Parameters_for_Uncertainty_Quantification_from_Physics-assisted_data-driven_stochastic_reduced-order_models_for_attribution_of_heterogeneous_stress_distributions_in_low-grain_polycrystals/28442144/1
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资源简介：
This file contains the parameters of the conditional Gaussian mixture model (CGMM) used for uncertainty quantification in different cases, including: mixture weights, mean vectors, and covariance matrices for bi-crystal, quad-crystal and octu-crystal configurations.
提供机构：
Bronkhorst, Curt A.; Zhang, Yinling; Chen, Nan; Dunham, Samuel D.
创建时间：
2025-02-19
