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arXiv 2103.07475nlin.SIhep-th
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机器可积性

Integrability ex machina

Sven Krippendorf, Dieter Lust, Marc Syvaeri

摘要:

确定一个动力系统是否是可积的通常是一个困难的任务,目前是在个案的基础上完成,需要大量的人力投入。在这里,我们提出并测试了一个自动化的方法来搜索存在的相关结构,拉克斯对和拉克斯连接分别。通过将此搜索公式化为优化问题,我们能够通过机器学习技术识别适当的结构。我们测试我们的方法对标准系统的经典可积性,发现我们可以挑出一些可积变形的系统。由于在定义一个Lax对我们的算法确定新的拉克斯对,可以很容易地验证分析的模糊性。

英文摘要:

Determining whether a dynamical system is integrable is generally a difficult task which is currently done on a case by case basis requiring large human input. Here we propose and test an automated method to search for the existence of relevant structures, the Lax pair and Lax connection respectively. By formulating this search as an optimization problem, we are able to identify appropriate structures via machine learning techniques. We test our method on standard systems of classical integrability and find that we can single out some integrable deformations of a system. Due to the ambiguity in defining a Lax pair our algorithm identifies novel Lax pairs which can be easily verified analytically.

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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