11 datasets found

Code and data for performing uncertainty quantification and sensitivity analysis of a multiphase flow model. The software computes the uncertainty in model predictions in the...

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient...

Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures...

We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in...

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident...

File descriptions: verif_inputs.zip and verif_case_inputs.zip Contain folders of raw PGM composites, containing needed input data for verification experiments, that is for the...

UQTestFuns is an open-source Python3 library of test functions commonly used within the applied uncertainty quantification (UQ) community. Specifically, the package provides:...

UQTestFuns is an open-source Python3 library of test functions commonly used within the applied uncertainty quantification (UQ) community. Specifically, the package provides:...

UQTestFuns is an open-source Python3 library of test functions commonly used within the applied uncertainty quantification (UQ) community. Specifically, the package provides:...