Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences

We propose a supervised deep learning algorithm based on low-discrepancy sequences as the training set. By combination of theoretical arguments and extensive numerical experiments we demonstrate that proposed significantly outperforms standard algorithms are randomly chosen data for problems in moderately high dimensions. The provides an efficient method building inexpensive surrogates many underlying maps context scientific computing.