Surrogate GNN

Surrogate graph neural networks (GNNs) are emerging as powerful tools for accelerating computationally expensive simulations across diverse scientific domains, from urban drainage to computational fluid dynamics. Research focuses on developing and scaling GNN architectures, such as MeshGraphNets, to handle large datasets and complex geometries, often incorporating physics-based constraints for improved accuracy and interpretability. These surrogate models offer significant advantages by providing faster, more efficient predictions while maintaining reasonable fidelity to underlying physical processes, enabling real-time applications and facilitating the analysis of large-scale problems.