5 citations · 12 across the 5 of their papers we have counts for
5 papers
Conformalized Physics-Informed Neural Networks
Lena Podina, Mahdi Torabi Rad, Mohammad Kohandel
Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neu…
Automatic detection of equiaxed dendrites using computer vision neural networks
A. Viardin, K. Noth, M. Torabi Rad +1
Equaixed dendrites are frequently encountered in solidification. They typically form in large numbers, which makes their detection, localization, and tracking practically impossibl…
Geometry of triple junctions during grain boundary premelting
M. Torabi Rad, G. Boussinot, M. Apel
Grain Boundaries (GB) whose energy is larger than twice the energy of the solid/liquid interface exhibit the premelting phenomenon, for which an atomically thin liquid layer develo…
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…
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…