38 citations · 56 across the 5 of their papers we have counts for
5 papers
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
Yusuke Yamazaki, Ali Harandi, Mayu Muramatsu +5
We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (…
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…
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…
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…
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…