2026-10-14T13:03:14Z https://www.scidb.cn/oai

10.57760/sciencedb.hjs.00557 2025-12-01T11:40:09Z

2025-12-01 A Physics-Informed Neural Network Source Iteration Approach for Improved Accuracy and Convergence in Neutron Diffusion Problems: Dataset and Benchmark Results doi:10.57760/sciencedb.hjs.00557 en 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.  Physics-Informed Neural Networks; Neutron Diffusion Equation; Extended Physics Informed Neural Networks; Accuracy Optimization Strategies; Multi-Region Subnetworks Cao Yun Zhuang Kun Chen Jingen RESTRICTED dataset http://www.doi.org/10.3724/j.0253-3219.2026.hjs.49.250351 Science Data Bank