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Improvements in computational and experimental capabilities are rapidlyincreasing the amount of scientific data that is routinely generated. Inapplications that are constrained by memory and computational intensity,excessively large datasets may hinder scientific discovery, making datareduction a critical component of data-driven methods. Datasets are growing intwo directions: the number of data points and their dimensionality. Whereasdimension reduction typically aims at describing each data sample onlower-dimensional space, the focus here is on reducing the number of datapoints. A strategy is proposed to select data points such that they uniformlyspan the phase-space of the data. The algorithm proposed relies on estimatingthe probability map of the data and using it to construct an acceptanceprobability. An iterative method is used to accurately estimate the probabilityof the rare data points when only a small subset of the dataset is used toconstruct the probability map. Instead of binning the phase-space to estimatethe probability map, its functional form is approximated with a normalizingflow. Therefore, the method naturally extends to high-dimensional datasets. Theproposed framework is demonstrated as a viable pathway to enable data-efficientmachine learning when abundant data is available. An implementation of themethod is available in a companion repository(https://github.com/NREL/Phase-space-sampling).

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows