用机器学习重新发现轨道力学

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Science and Technology Pub Date : 2023-10-09 DOI:10.1088/2632-2153/acfa63
Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia
{"title":"用机器学习重新发现轨道力学","authors":"Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia","doi":"10.1088/2632-2153/acfa63","DOIUrl":null,"url":null,"abstract":"Abstract We present an approach for using machine learning to automatically discover the governing equations and unknown properties (in this case, masses) of real physical systems from observations. We train a ‘graph neural network’ to simulate the dynamics of our Solar System’s Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to correctly infer an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton’s law of gravitation. The key assumptions our method makes are translational and rotational equivariance, and Newton’s second and third laws of motion. It did not, however, require any assumptions about the masses of planets and moons or physical constants, but nonetheless, they, too, were accurately inferred with our method. Naturally, the classical law of gravitation has been known since Isaac Newton, but our results demonstrate that our method can discover unknown laws and hidden properties from observed data.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"43 1","pages":"0"},"PeriodicalIF":6.3000,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"Rediscovering orbital mechanics with machine learning\",\"authors\":\"Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia\",\"doi\":\"10.1088/2632-2153/acfa63\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract We present an approach for using machine learning to automatically discover the governing equations and unknown properties (in this case, masses) of real physical systems from observations. We train a ‘graph neural network’ to simulate the dynamics of our Solar System’s Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to correctly infer an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton’s law of gravitation. The key assumptions our method makes are translational and rotational equivariance, and Newton’s second and third laws of motion. It did not, however, require any assumptions about the masses of planets and moons or physical constants, but nonetheless, they, too, were accurately inferred with our method. Naturally, the classical law of gravitation has been known since Isaac Newton, but our results demonstrate that our method can discover unknown laws and hidden properties from observed data.\",\"PeriodicalId\":33757,\"journal\":{\"name\":\"Machine Learning Science and Technology\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2023-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine Learning Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2632-2153/acfa63\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Learning Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2632-2153/acfa63","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 38

摘要

我们提出了一种利用机器学习从观测中自动发现真实物理系统的控制方程和未知属性(在这种情况下,质量)的方法。我们训练了一个“图形神经网络”来模拟太阳系太阳、行星和大型卫星的动态,这些数据来自30年的轨迹数据。然后,我们使用符号回归正确地推断出由神经网络隐式学习的力定律的解析表达式,我们的结果表明,这相当于牛顿的万有引力定律。我们的方法所做的关键假设是平移和旋转等变性,以及牛顿的第二和第三运动定律。然而,它不需要对行星和卫星的质量或物理常数进行任何假设,但尽管如此,它们也可以用我们的方法准确地推断出来。当然,经典的万有引力定律自艾萨克·牛顿(Isaac Newton)以来就已经为人所知,但我们的结果表明,我们的方法可以从观测数据中发现未知的定律和隐藏的特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Rediscovering orbital mechanics with machine learning
Abstract We present an approach for using machine learning to automatically discover the governing equations and unknown properties (in this case, masses) of real physical systems from observations. We train a ‘graph neural network’ to simulate the dynamics of our Solar System’s Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to correctly infer an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton’s law of gravitation. The key assumptions our method makes are translational and rotational equivariance, and Newton’s second and third laws of motion. It did not, however, require any assumptions about the masses of planets and moons or physical constants, but nonetheless, they, too, were accurately inferred with our method. Naturally, the classical law of gravitation has been known since Isaac Newton, but our results demonstrate that our method can discover unknown laws and hidden properties from observed data.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
期刊最新文献
Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net. Optimizing ZX-diagrams with deep reinforcement learning DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data Equivariant tensor network potentials Masked particle modeling on sets: towards self-supervised high energy physics foundation models
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1