We introduce a novel framework for the automatic discovery of one-parameter subgroups ($H_{\gamma}$) of $SO(3)$ and, more generally, $SO(n)$. One-parameter subgroups of $SO(n)$ are crucial in a wide range of applications, including robotics, quantum mechanics, and molecular structure analysis. Our method utilizes the standard Jordan form of skew-symmetric matrices, which define the Lie algebra of $SO(n)$, to establish a canonical form for orbits under the action of $H_{\gamma}$. This canonical form is then employed to derive a standardized representation for $H_{\gamma}$-invariant functions. By learning the appropriate parameters, the framework uncovers the underlying one-parameter subgroup $H_{\gamma}$. The effectiveness of the proposed approach is demonstrated through tasks such as double pendulum modeling, moment of inertia prediction, top quark tagging and invariant polynomial regression, where it successfully recovers meaningful subgroup structure and produces interpretable, symmetry-aware representations.
翻译:我们提出了一种新颖的框架,用于自动发现SO(3)及更一般地SO(n)的单参数子群(H_{\gamma})。SO(n)的单参数子群在机器人学、量子力学和分子结构分析等广泛领域中具有关键应用。我们的方法利用斜对称矩阵的标准若尔当形式(这些矩阵定义了SO(n)的李代数),为H_{\gamma}作用下的轨道建立规范形式。随后,该规范形式被用于推导H_{\gamma}不变函数的标准化表示。通过学习适当的参数,该框架能够揭示潜在的单参数子群H_{\gamma}。通过双摆建模、转动惯量预测、顶夸克标记及不变多项式回归等任务,我们验证了所提方法的有效性,在这些任务中,该方法成功恢复了有意义的子群结构,并生成了可解释的、具有对称性感知的表示。