Graph-Parallel Training

A graph-parallel framework is being developed that enables graph neural networks to be trained on large unstructured CFD meshes by distributing results of a single simulation across multiple GPUs. During training, a dedicated synchronization layer exchanges information between graph partitions, improving prediction accuracy while enabling scalable GNN inference for large urban flow domains. The approach is being tested for the prediction of urban flow fields.