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,通过探测器获得的数据不可避免地很吵。对于已经提出的基于数据的物理知情神经网络(DEPINN)\citep{DEPINN},我们从几个角度调查DEPINN在计算中子扩散乙基值问题方面的表现,因为先前的数据包含不同程度的噪音。此外,为了减少噪音的影响,改进对以前噪音数据的利用,我们提出了创新的间隔损失功能,并提供了一些严格的数学证明。DEPINN的强性通过大量的数字结果对两个典型的基准问题进行了研究,而拟议的间隔损失功能的有效性通过比较得到了证明。这份文件证实了改进的DEPINN在核反应堆物理学中实际工程应用的可行性。</s>