38 citations · 56 across the 5 of their papers we have counts for
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cs.LG2024★ 38 cited
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 (…
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…