The provided text discusses DeepMind’s advancements in machine learning (ML), particularly in fluid dynamics simulation, and explores their potential application to electricity grid operations. It highlights Graph Network-based Simulators (GNS) and MeshGraphNets as key ML techniques that can simulate complex physical processes with significantly increased speed and accuracy compared to traditional methods. The text then draws an analogy between fluid flow and electricity flow in power grids, proposing that similar ML techniques could revolutionise areas such as power flow optimisation, renewable energy integration, enhanced weather forecasting for grid resilience, and fault detection through digital twin technology. While acknowledging limitations like data requirements and the need for regulatory acceptance, the overall assessment suggests that ML offers substantial short-term benefits like improved forecasting and long-term potential for autonomous grid management.