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arXiv Machine Learning By Qin Hang, Yangdi Yi, Jiayi Li, Xu Wang, Heng Zhang

Joint Initialization of Flux Networks and Effective Multiplication Factor for Physics-Informed Neural Networks Solving Neutron Diffusion Problems

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The paper introduces JI-PINN, a joint initialization strategy for physics-informed neural networks that simultaneously initializes neutron flux and the effective multiplication factor (keff) using a low-resolution approximate solution. By jointly optimizing both quantities under physical constraints, the method achieves significant reductions in computational time—up to nearly 50%—across various benchmark neutron diffusion problems while maintaining accuracy. It also reduces anomalous keff deviations, offering a more robust approach to solving K‑eigenvalue problems with PINNs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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