Research from Harvard University Yields New Findings on Machine Learning (Recons tructing S-matrix Phases with Machine Learning)
哈佛大学的研究产生了机器学习的新发现（用机器学习重建s矩阵相位）
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摘要
由机器人与机器学习每日新闻的新闻记者兼新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsR X Consorters从哈佛大学发来的消息，研究表明：“S-矩阵Bootstr AP程序的一个重要元素是S-矩阵元素的模量与它的相之间的关系。”新闻记者从哈佛大学的研究中得到一句话：“统一性通过积分方程将它们联系起来。即使在弹性散射的最简单情况下，本文应用现代机器学习技术研究了单元性约束,发现对于给定的模,当存在一个pha se时,机器学习一般可以很好地重构到较高的精度,而且,在此基础上,本文提出了一种新的方法.重建算法的损失提供了一个很好的代理，说明给定的模量是否完全符合统一性。此外，我们还研究了多相是否可以与单个模量一致的问题，寻找新的相位模糊解。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Harvard University by NewsR x correspondents, research stated, “An important element of the S-matrix bootstr ap program is the relationship between the modulus of an S-matrix element and it s phase.”The news correspondents obtained a quote from the research from Harvard Universi ty: “Unitarity relates them by an integral equation. Even in the simplest case o f elastic scattering, this integral equation cannot be solved analytically and n umerical approaches are required. We apply modern machine learning techniques to studying the unitarity constraint. We find that for a given modulus, when a pha se exists it can generally be reconstructed to good accuracy with machine learni ng. Moreover, the loss of the reconstruction algorithm provides a good proxy for whether a given modulus can be consistent with unitarity at all. In addition, w e study the question of whether multiple phases can be consistent with a single modulus, finding novel phase-ambiguous solutions.”
Key words
Harvard University/Cyborgs/Emerging Te chnologies/Machine Learning
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