3 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 3 cited
On Theory-training Neural Networks to Infer the Solution of Highly Coupled Differential Equations
M. Torabi Rad, A. Viardin, M. Apel
Deep neural networks are transforming fields ranging from computer vision to computational medicine, and we recently extended their application to the field of phase-change heat tr…
physics.comp-ph2020
Comparing mesoscopic models for dendritic growth
Damien Tourret, Laszlo Sturz, Alexandre Viardin +1
We present a quantitative benchmark of multiscale models for dendritic growth simulations. We focus on approaches based on phase-field, dendritic needle network, and grain envelope…
physics.app-ph2019★ 1 cited
Theory-training deep neural networks for an alloy solidification benchmark problem
M. Torabi Rad, A. Viardin, G. J. Schmitz +1
Deep neural networks are machine learning tools that are transforming fields ranging from speech recognition to computational medicine. In this study, we extend their application t…