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存在噪声。针对先前提出的数据驱动物理知识神经网络(DEPINN)\citep{DEPINN},我们从几个角度研究DEPINN计算中子扩散本征值问题在存在不同噪声大小的先验数据时的性能。进一步,为了减少噪声影响并提高噪声先验数据的利用效率,我们提出了创新的区间损失函数并给出了一些严格的数学证明。通过大量的数值结果对两个典型基准问题进行了DEPINN的鲁棒性检验,并通过比较证明了所提出的区间损失函数的有效性。本文证实了改进后的DEPINN在核反应堆物理的实际工程应用中的可行性。