【论文速递】Data-driven discovery of intrinsic Lie point symmetries of differential equation

英文标题：Data-driven discovery of intrinsic Lie point symmetries of differential equation systems

arXiv编号：2611.08421
作者：Ilker Gurcan, Kadir Ustun, Ramazan Gokberk Cinbis
来源：arXiv
原文链接：https://arxiv.org/abs/2611.08421

中文摘要：
Discovering continuous symmetries directly from differential-equation data can reveal geometric structure for model reduction and scientific machine learning without symbolic determining equations or a closed-form representation of the governing system. We formulate intrinsic Lie point-symmetry discovery as an operator-theoretic problem on sampled jet-space data. Admissible generators are identified from a sampled determining operator and separated from redundant, solution-dependent, and geometrically trivial directions. The framework combines prolongation, sequential subspace restrictions, and a fixed nonlinear dictionary linear in trainable coefficients. A rank-stabilization preflight test diagnoses systems whose represented intrinsic symmetry algebra does not stabilize under dictionary refinement. On a controlled differential-equation benchmark with an analytically known 11-dimensional represented intrinsic symmetry algebra, the method recovers the complete represented generator set, reproduces its Lie-bracket structure to numerical precision, and approximates a known generator beyond the training domain. Here we show that explicit intrinsic continuous-symmetry generators can be recovered from data obtained from exact or approximate differential systems.

本文由华人空间 AI 论文助手自动抓取翻译，仅供学术交流。