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

Code implementation of the Bayesian autoencoder from Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants

物理建模

不确定性量化

Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under $\ell _1$ constraints allows joint discoveries of governing equations and latent coordinate systems from

NIAID Data Ecosystem00

Supporting Data: "Uncertainty In Sea Level Rise Projections Due To The Dependence Between Contributors"

海平面建模

不确定性量化

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

Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning

脑循环模拟

不确定性量化

Data and code underlying the findings reported in the paper titled "Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machin

Zenodo2022-05-25 更新00

FLOPs and energy consumption comparison of uncertainty quantification modules.

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