Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example to demonstrate how both of those tasks can be accomplished with machine learning techniques. We propose loss functions whose minimization results in true models that are also beautiful as measured by three different criteria — uniformity, sparsity, or symmetry.

中文翻译：

---

用机器学习探索理论景观的真与美


理论物理学家通过 i）建立理论模型和 ii）确定模型参数来描述自然。后一步涉及到拟合现有实验数据和满足美丽、自然等抽象标准的双重方面。我们使用汤川夸克扇区作为玩具示例来演示如何通过机器学习技术来完成这两个任务。我们提出了损失函数，其最小化会产生真实的模型，根据三个不同的标准（均匀性、稀疏性或对称性）来衡量，这些模型也很漂亮。