Graph neural networks coupled with neural ODEs can forecast complex physical systems under partial observability at speeds enabling real-time control, while learning physically meaningful relationships from limited experimental data.
This paper develops a physics-informed neural network that combines graph neural networks with neural differential equations to predict thermal-hydraulic states in nuclear reactors, even at locations without sensors. The model runs 105× faster than simulation and can be adapted to real experimental data with minimal retraining, enabling real-time control of advanced reactors.