A GNN-ODE surrogate forecasts reactor thermal-hydraulics under partial observability, achieving low MAE on held-out transients, fast inference, and recovery of a physical Reynolds-number exponent after fine-tuning on limited experimental data.
NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations
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Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability
A GNN-ODE surrogate forecasts reactor thermal-hydraulics under partial observability, achieving low MAE on held-out transients, fast inference, and recovery of a physical Reynolds-number exponent after fine-tuning on limited experimental data.