20 citations · 80 across the 13 of their papers we have counts for
13 papers
A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks
Veronica Saz Ulibarrena, Philipp Horn, Simon Portegies Zwart +3
Simulating the evolution of the gravitational N-body problem becomes extremely computationally expensive as N increases since the problem complexity scales quadratically with the n…
Comparison of neural closure models for discretised PDEs
Hugo Melchers, Daan Crommelin, Barry Koren +2
Neural closure models have recently been proposed as a method for efficiently approximating small scales in multiscale systems with neural networks. The choice of loss function and…
On the influence of stochastic roundoff errors and their bias on the convergence of the gradient descent method with low-precision floating-point computation
Lu Xia, Stefano Massei, Michiel E. Hochstenbach +1
When implementing the gradient descent method in low precision, the employment of stochastic rounding schemes helps to prevent stagnation of convergence caused by the vanishing gra…
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…
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…
Improved stochastic rounding
Lu Xia, Martijn Anthonissen, Michiel Hochstenbach +1
Due to the limited number of bits in floating-point or fixed-point arithmetic, rounding is a necessary step in many computations. Although rounding methods can be tailored for diff…