20 citations · 80 across the 13 of their papers we have counts for
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physics.flu-dyn2021
Intrusive deconvolutional neural networks for enhancing PIC/FLIP solutions
Y. van Halder, B. Sanderse, B. Koren
Traditional fluid flow predictions require large computational resources. Despite recent progress in parallel and GPU computing, the ability to run fluid flow predictions in real-t…
cs.LG2021★ 3 cited
A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision
Lu Xia, Martijn Anthonissen, Michiel Hochstenbach +1
Conventional stochastic rounding (CSR) is widely employed in the training of neural networks (NNs), showing promising training results even in low-precision computations. We introd…