We propose a method to explore the flavor structure of leptons using diffusion models, which are known as one of generative artificial intelligence (generative AI). We consider a simple extension of the Standard Model with the type I seesaw mechanism and train a neural network to generate the neutrino mass matrix. By utilizing transfer learning, the diffusion model generates 104 solutions that are consistent with the neutrino mass squared differences and the leptonic mixing angles. The distributions of the CP phases and the sums of neutrino masses, which are not included in the conditional labels but are calculated from the solutions, exhibit non-trivial tendencies. In addition, the effective mass in neutrinoless double beta decay is concentrated near the boundaries of the existing confidence intervals, allowing us to verify the obtained solutions through future experiments. An inverse approach using the diffusion model is expected to facilitate the experimental verification of flavor models from a perspective distinct from conventional analytical methods.

翻译：我们提出一种利用扩散模型探索轻子味结构的方法，扩散模型是生成式人工智能（生成式AI）的一种。我们在包含I型跷跷板机制的标准模型简单扩展下，训练神经网络生成中微子质量矩阵。通过迁移学习，该扩散模型生成了104个与中微子质量平方差及轻子混合角相一致的解。未包含在条件标签中但通过解计算得到的CP相角和中微子质量和分布呈现出非平凡的趋势。此外，无中微子双贝塔衰变中的有效质量集中在现有置信区间边界附近，这使我们能够通过未来实验验证所得解。使用扩散模型的逆向方法有望从不同于传统解析方法的视角，促进味模型的实验验证。