Synthetic Datasets for Numeric Uncertainty Quantification

Abstract

In this paper, we propose ten synthetic datasets for point prediction and numeric uncertainty quantification (UQ). These datasets are split into train, validation, and test sets for model benchmarking. Equations and the description of each dataset are provided in detail. We also present representative shallow neural network (NN) training and Random Vector Functional Link (RVFL) training examples. Both training train models for the point prediction. We perform uncertainty quantification with the consideration of a gaussian and homoscedastic distribution. As the distribution consideration and models are rudimental, much room exists for further explorations and improvements. The dataset and scripts are available at the following link: https://github.com/dipuk0506/UQ-Data