A Novel Physics-Informed Graph Convolutional Reduced-Order Model for Fluid Flow on Unstructured Meshes
Findings demonstrate that Phys-GCN provides an accurate and efficient graph-based and physics-informed surrogate for steady flow-field reconstruction on non-uniform, unstructured meshes, thereby laying a solid foundation for future extensions to more complex three-dimensional and compressible flow configurations.