GLaDiT: Graph-Local Latent Diffusion Transformer for Flow Generation

Predicting unsteady fluid flow on irregular meshes requires resolving coupled velocity and pressure dynamics over extended horizons. While graph-based simulators accommodate unstructured geometries natively, autoregressive rollouts suffer from compounding error accumulation. Conversely, trajectory diffusion models bypass recursive drift but rely on regular Euclidean grids, requiring lossy interpolations that distort mesh connectivity and inflate memory overhead. We introduce GLaDiT (Graph-Local Latent Diffusion Transformer), a graph-native Diffusion Transformer for flow generation. Operating directly on coarse mesh representations via a pretrained graph autoencoder, GLaDiT concurrently refines an entire multi-frame trajectory block in parallel, eliminating intermediate grid projections and recursive error feedback. Within the generative backbone, graph-local spatiotemporal attention pairs 1-hop spatial graph communication with global temporal attention, maintaining sparse per-block computation while structurally permitting domain-wide dependency paths across network depth. Evaluations demonstrate that GLaDiT achieves leading accuracy on most evaluation metrics across multiple datasets, while offering an adjustable compute-accuracy budget and reducing peak inference memory by 18.5× compared to Cartesian grid trajectory diffusion.