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资源简介：
Mindlin Plate data used for training DNN surrogate model for Uncertainty Quantification.
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提供机构：
Zhang, Lixiang相关数据集
METR-LA
Traffic datasets for ICCPS "Uncertainty Quantification for Physics-Informed Traffic Graph Networks"
Figshare2025-02-05 更新60
Supporting Data: "Uncertainty In Sea Level Rise Projections Due To The Dependence Between Contributors"
These files contain the data analyzed in Le Bars 2018. The paper is available on EarthArXiv (https://eartharxiv.org/uvw3s/) and was submitted to Earth's Future. The NetCDF files contain the Probabili
Zenodo2020-09-20 更新30
Demographic description of the sample.
Modeling decision-making under uncertainty typically relies on quantitative outcomes. Many decisions, however, are qualitative in nature, posing problems for traditional models. Here, we aimed to mode
NIAID Data Ecosystem00
Monte Carlo simulation and correction factors.
Compressed file containing a description of the methodology used to perform the Monte Carlo simulations and raw data from Figs 4 and 5. (ZIP)
Figshare2018-05-15 更新40
MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select
NIAID Data Ecosystem20
