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arXiv 2201.13133nlin.SInlin.AOphysics.acc-ph
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机器辅助发现可积辛映射

Machine-assisted discovery of integrable symplectic mappings

Timofey Zolkin, Yaroslav Kharkov, Sergei Nagaitsev

AI总结:

本文提出一种基于轨迹形状分析多边形不变量的自动化方法,用于发现平面可积辛映射,重新发现已知映射并找到超过100个新可积族,且提出构造近可积系统的方法。

摘要:

我们提出了一种新的自动化方法,用于寻找平面上的可积辛映射。这些动力系统具有与守恒量(即运动积分)存在相关的隐藏对称性。该算法的核心思想基于以下知识:可积系统在相空间中的演化被限制在较低维的子流形上。我们将自己限制在多边形运动不变量上,分析单个轨迹的形状,从而成功地区分可积运动与混沌情形。例如,我们的方法重新发现了一些著名的McMillan-Suris可积映射和离散Painlevé方程。总共,我们提出并分析了超过100个新的可积族;其中一些在参数空间中是孤立的,而另一些则是具有一个参数(或参数之比)为连续或离散的族。在论文末尾,我们通过引入离散微扰理论,提出了新发现的映射如何与一般的二维辛映射相关联,并提出了一种基于具有多边形不变量的映射来构造光滑近可积动力系统的方法。

英文摘要:

We present a new automated method for finding integrable symplectic maps of the plane. These dynamical systems possess a hidden symmetry associated with an existence of conserved quantities, i.e. integrals of motion. The core idea of the algorithm is based on the knowledge that the evolution of an integrable system in the phase space is restricted to a lower-dimensional submanifold. Limiting ourselves to polygon invariants of motion, we analyze the shape of individual trajectories thus successfully distinguishing integrable motion from chaotic cases. For example, our method rediscovers some of the famous McMillan-Suris integrable mappings and discrete Painlevé equations. In total, over 100 new integrable families are presented and analyzed; some of them are isolated in the space of parameters, and some of them are families with one parameter (or the ratio of parameters) being continuous or discrete. At the end of the paper, we suggest how newly discovered maps are related to a general 2D symplectic map via an introduction of discrete perturbation theory and propose a method on how to construct smooth near-integrable dynamical systems based on mappings with polygon invariants.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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