P-K-GCN

P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for spatiotemporal super-resolution

Researchers propose P-K-GCN, a framework combining graph convolutional networks, Koopman operator theory, and physics-informed loss functions for spatiotemporal super-resolution on irregular geometries. The method linearizes nonlinear dynamics in a latent space and enforces physical constraints to improve reconstruction fidelity. Theoretical analysis claims guaranteed error reduction via Rademacher complexity bounds. The framework is evaluated on reconstructing high-resolution cardiac electrodynamics from sparse 3D heart geometry measurements.