A Physics-Informed Neural Network Source Iteration Approach for Improved Accuracy and Convergence in Neutron Diffusion Problems: Dataset and Benchmark Results
收藏科学数据银行2025-12-01 更新2026-04-23 收录
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https://www.scidb.cn/detail?dataSetId=2f10b77a0ffe4dc9a4b4d40f51b604f9
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资源简介：
This dataset contains complete case results of solving the two-dimensional two group neutron diffusion equation using the PINN source iterative method. Research on the PINN framework built on PyTorch, covering three typical testing scenarios: (1) the five region problem of smooth transition; (2) TWIGL benchmark questions (including two sets of results before and after section correction); (3) XPINN Three Partition Example. The dataset provides high-resolution images, training logs, and key model outputs, which can be used for validation research of methods such as PINN, RAR, dynamic weighting, XPINN, etc.
提供机构：
Shanghai Institute of Applied Physics; Nanjing University of Aeronautics and Astronautics
创建时间：
2025-12-01
