Efficient model-order reduction via graph autoencoders and operator learning for systems with sharp gradients using a novel point cloud error metric for performance assessment

This study investigates the use of graph autoencoders, a class of graph neural networks (GNNs), to perform model-order reduction (MOR) on high-dimensional systems characterized by sharp gradients for which conventional linear approximations, such as proper orthogonal decomposition (POD) bases, are l