In practical engineering experiments, the data obtained through detectors are inevitably noisy. For the already proposed data-enabled physics-informed neural network (DEPINN) \citep{DEPINN}, we investigate the performance of DEPINN in calculating the neutron diffusion eigenvalue problem from several perspectives when the prior data contain different scales of noise. Further, in order to reduce the effect of noise and improve the utilization of the noisy prior data, we propose innovative interval loss functions and give some rigorous mathematical proofs. The robustness of DEPINN is examined on two typical benchmark problems through a large number of numerical results, and the effectiveness of the proposed interval loss function is demonstrated by comparison. This paper confirms the feasibility of the improved DEPINN for practical engineering applications in nuclear reactor physics.

翻译：在实际工程实验中，通过探测器获取的数据不可避免地包含噪声。针对已提出的数据驱动物理信息神经网络（DEPINN）\citep{DEPINN}，我们从多个角度研究了在先验数据包含不同噪声尺度时，DEPINN在计算中子扩散特征值问题中的性能表现。此外，为降低噪声影响并提高含噪先验数据的利用效率，我们创新性地提出了区间损失函数，并给出了严谨的数学证明。通过大量数值结果，在两类典型基准问题上检验了DEPINN的鲁棒性，并通过对比验证了所提区间损失函数的有效性。本文证实了改进后的DEPINN在核反应堆物理实际工程应用中的可行性。