Real-time supervisory control of advanced reactors requires accurate forecasting of plant-wide thermal-hydraulic states, including locations where physical sensors are unavailable. Meeting this need calls for surrogate models that combine predictive fidelity, millisecond-scale inference, and robustness to partial observability. In this work, we present a physics-informed message-passing Graph Neural Network coupled with a Neural Ordinary Differential Equation (GNN-ODE) to addresses all three requirements simultaneously. We represent the whole system as a directed sensor graph whose edges encode hydraulic connectivity through flow/heat transfer-aware message passing, and we advance the latent dynamics in continuous time via a controlled Neural ODE. A topology-guided missing-node initializer reconstructs uninstrumented states at rollout start; prediction then proceeds fully autoregressively. The GNN-ODE surrogate achieves satisfactory results for the system dynamics prediction. On held-out simulation transients, the surrogate achieves an average MAE of 0.91 K at 60 s and 2.18 K at 300 s for uninstrumented nodes, with $R^2$ up to 0.995 for missing-node state reconstruction. Inference runs at approximately 105 times faster than simulated time on a single GPU, enabling 64-member ensemble rollouts for uncertainty quantification. To assess sim-to-real transfer, we adapt the pretrained surrogate to experimental facility data using layerwise discriminative fine-tuning with only 30 training sequences. The learned flow-dependent heat-transfer scaling recovers a Reynolds-number exponent consistent with established correlations, indicating constitutive learning beyond trajectory fitting. The model tracks a steep power change transient and produces accurate trajectories at uninstrumented locations.

翻译：先进反应堆的实时监督控制需要准确预测全厂热工水力状态，包括缺乏物理传感器的位置。这一需求要求替代模型同时具备预测保真度、毫秒级推理能力和对部分可观测性的鲁棒性。本文提出一种融合物理信息的消息传递图神经网络与神经常微分方程（GNN-ODE），以同时满足上述三个要求。我们将整个系统建模为有向传感器图，其边通过流动/传热感知的消息传递编码水力连通性，并通过受控神经ODE在连续时间内推进潜在动力学。拓扑引导的缺失节点初始化器在滚动开始阶段重建未仪表化状态，随后完全自回归地执行预测。GNN-ODE替代模型在系统动力学预测中取得了令人满意的结果。在保留的仿真瞬态测试中，该替代模型对未仪表化节点在60秒和300秒时分别实现0.91 K和2.18 K的平均绝对误差（MAE），缺失节点状态重建的$R^2$高达0.995。单GPU推理速度约为仿真时间的105倍，支持64成员集合滚动以实现不确定性量化。为评估仿真到现实的迁移，我们使用层级判别微调方法，仅用30条训练序列将预训练替代模型适配至实验设施数据。学习到的流量依赖型传热标度恢复出与已知关联式一致的雷诺数指数，表明其实现了超越轨迹拟合的本构学习。该模型可跟踪陡峭功率变化瞬态，并在未仪表化位置生成精确轨迹。