mlsanityData to 7 Oct 2026

On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem

arXiv 2303.08455 ↗First posted 15 Mar 2023cs.LG physics.data-ancited by 2

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Abstract (arXiv)

In practical engineering experiments, the data obtained through detectors are inevitably noisy. For the already proposed data-enabled physics-informed neural network (DEPINN) citepDEPINN, 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 53 references that are in the map. First posted on arXiv 15 Mar 2023.

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20 of 53 references.

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8 more
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraintcited by 5
  2. 2025Artificial Intelligence in Reactor Physics: Current Status and Future Prospectscited by 1