用生成模型从粒子数据中学习中子源分布，训练后可快速高效采样。
Machine Learning for neutron source distributions
- 基于概率生成模型，从蒙特卡洛粒子列表学习中子源分布
- 训练后无需原始粒子数据，采样速度快且不占内存
- 对比四种生成模型，验证了该方法的有效性与可行性
随着机器学习的进展，我们提出一种新方法，通过概率生成模型估计中子源分布。该方法仅在模型训练阶段需要蒙特卡洛粒子列表，一旦源分布被学习，模型即脱离原始粒子列表，实现高效、快速且无内存开销的后续采样。评估了多种生成模型的性能，包括变分自编码器（VAE）、归一化流（Normalizing Flow）、生成对抗网络（GAN）和去噪扩散模型（Denoising Diffusion Model），并将其与现有源分布估计方法进行比较，讨论各方法的优劣。结果表明，概率生成模型能够有效建模中子源分布，为该领域进一步发展提供了可能。
原文摘要 · Abstract (English)
In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative adversarial network, and a denoising diffusion model. These approaches are then compared to existing source distribution estimations, and the advantages and disadvantages of each approach are discussed. The results demonstrate that source distributions can be modeled through the use of probabilistic generative models, which paves the way for further advancements in this field.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。