Graph Network Simulator

Graph Network Simulators (GNS) are machine learning models designed to efficiently and accurately simulate physical systems by representing them as graphs, where nodes represent objects and edges represent interactions. Current research focuses on improving GNS scalability to handle complex, real-world scenarios with numerous objects and diverse interactions, often employing graph neural networks and incorporating physical constraints or sensor data to enhance accuracy and generalization. This approach offers significant speedups over traditional physics-based simulators, enabling faster solutions to forward and inverse problems in diverse fields like robotics, materials science, and subsurface flow modeling, ultimately accelerating scientific discovery and engineering design.