most citedData-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2026

HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks

SiHun Lee, Dong-Hyuk Park, Taesoo Bang +1

Machine-learned physical surrogate models have become promising alternatives to mesh-based numerical solvers. Among them, graph neural networks (GNNs) are well suited for represent…

cs.LG2023★ 1 cited

Data-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder

SiHun Lee, Sangmin Lee, Kijoo Jang +2

A data-driven parametric model order reduction (MOR) method using a deep artificial neural network is proposed. The present network, which is the least-squares hierarchical variati…