Real-time monitoring of safety-critical interior states remains an open problem in energy systems where physical instrumentation is infeasible. Existing approaches rely on explicit governing equations, finite-dimensional state vectors, or per-instance retraining, which prevents mesh-independent, field-level inference at arbitrary interior coordinates under real-time constraints. We introduce operator-based virtual sensing for nuclear-grade thermal-fluid systems: we use the neural-operator framework to learn solution operators that map sparse boundary measurements to coupled internal fields in physically inaccessible regions, framing the problem class explicitly to distinguish it from classical state estimation and pointwise soft sensing. We instantiate this framework with MIMONet, a branch-trunk operator extended with three practical choices: multi-modal branch encoders for heterogeneous (scalar and function-valued) inputs; multiplicative branch fusion to preserve the bilinear PDE coupling structure; and shared-latent multi-field decoding with per-channel basis projections at the trunk's final layer. Evaluated across escalating complexity, from canonical lid-driven cavity flow to pressurized water reactor subchannels to fully coupled heat exchangers, MIMONet achieves below 5% relative errors and sub-millisecond inference on data-center accelerators (0.35 ms / 46 mJ per heat-exchanger inference on an NVIDIA H200, and sub-millisecond across the A40-H200-GH200 range), while remaining stable under 50% sensor noise. By staying accurate as geometric confinement and physics coupling intensify, MIMONet shows that operator-based virtual sensing can restore observability where physical instrumentation fails, establishing simulation-based feasibility within the evaluated operating envelopes as a step toward future experimental and cross-solver validation for safety-critical energy systems.

翻译：对安全关键内部状态的实时监测仍是物理仪器无法部署的能源系统中的开放问题。现有方法依赖显式控制方程、有限维状态向量或逐实例重新训练，这阻碍了实时约束下任意内部坐标处网格无关的现场级推理。我们提出面向核级热工流体的算子型虚拟传感方法：采用神经算子框架学习将稀疏边界测量映射至物理不可达区域耦合内场的解算子，并通过显式界定问题类别以区别于经典状态估计与逐点软传感。我们以MIMONet实例化该框架——这是一种经三项实用扩展的分支-主干算子：适用于异构（标量与函数值）输入的多模态分支编码器；用于保持双线性偏微分方程耦合结构的乘法分支融合；以及主干网络末层带逐通道基投影的共享隐变量多场解码。在从标准顶盖驱动方腔流到压水堆子通道再到全耦合换热器的递进复杂度评估中，MIMONet在数据中心加速器上实现了低于5%的相对误差与亚毫秒级推理（在NVIDIA H200上换热器推理耗时0.35毫秒/能耗46毫焦耳，且在整个A40-H200-GH200系列中保持亚毫秒级），并在50%传感器噪声下仍保持稳定。随着几何约束与物理耦合增强仍能保持精度，MIMONet表明基于算子的虚拟传感可恢复物理仪器失效处的可观测性，从而在评估运行包络内建立基于仿真的可行性，为安全关键能源系统的未来实验与跨求解器验证奠定基础。