On Multidisciplinary Potential Applications of Gauge Theories

Jean-Pierre Magnot
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Abstract

We review how gauge theories, initial introduced for classical mechanics and quantum led theory, seem to apply to many fields of research such as information theory, computer science, economy, biology. This non-exhaustive list raises natural question on future developments of this theory. Short Communication How gauge theories appear in information theory and economics Basically, the mathematical structure of groups is among the best adapted for describing transformations and moves. A path on a group can encode the evolution of a dynamical system, or the moves of an exterior observer with respect to a given system. In the theory of quantum fields, more general objects called principal bundles, which consist in a total space P with fibers isomorphic to a (Lie) group G over a base (simplicial complex or manifold). M For one of the most simple settings, one can see when the base is a manifold (we call them continuum gauge theories) [1-5]. When the base is a simplicial complex (or a lattice), the models are called discrete gauge theories. They stand formally as an integrated version of the previous ones which (more or less formally again). The physical study consists in solving equations on P or after quantization into fields, in minimizing so-called action functionals. These action functionals are in fact acting on connections, which are infinitesimal expressions of local slices. They can be understood also as differential operators. These delicate settings coming from physics have, in the last twenty years, found two applications to our knowledge: In so-called " geometric arbitrage theory» for managing portfolios in financial markets [2,6-10], where fiber bundles or principal bundles describe, along their fibers and on the base, the external and internal parameters for the prediction of the evolution of the portfolio. Fiber bundles over a manifold described in [9] are related to principal bundles in a way described in [10]. In so-called approximate reasoning in pairwise comparisons where complete studies can be performed when * G + =  and with normalized objects, with very recent axiomatization [10-19]. However, the need of so-called non-numerical ranking appeals the use of non-abelian groups G and of mathematical tools coming from discrete gauge theories [12,18]. Gauge wraith in biology and neurology The aim of this short article, giving the opinion of the sole author, is to mention that there are some hidden aspects of neurology and biology which reveal, in some kind of matching mirror process, the presence of gauge effects. This aspect is not actually truly confirmed, it is more-or-less at a step of conceptualization, and that is the reason why we talk of gauge wraith. Other authors have already mentioned the similarities of models coming from biology and neuronal science [11,15,16], each time with very mild interpretations of classical facts. From a heuristic viewpoint, this is not so surprising since «physics of life» deal with entities interacting with each other, exactly like elementary particles of nuclear physics interact. At the microscopic scale, a simplicial structure arise naturally by linking an entity (neuron, molecule, cell) with each of its neighbours making discrete gauge theories appropriate, and at macroscopic level, a first approximation of phenomena can rise form continuum gauge theories. However, as a last remark, the mathematical theory of discretization of gauge theories has to be questioned very seriously, because the classical scheme based on leads to mathematical problems while other still non studied ways to discretize a continuum model can be proposed [17].
论规范理论的多学科潜在应用
我们回顾了最初为经典力学和量子理论引入的规范理论如何应用于许多研究领域,如信息论、计算机科学、经济学、生物学。这个不详尽的清单自然会对这一理论的未来发展提出疑问。基本上,群体的数学结构是最适合描述转变和移动的。群上的路径可以编码动力系统的演化,或者外部观察者相对于给定系统的移动。在量子场理论中,称为主束的更一般的物体,它们存在于总空间P中,其纤维同构于基(简复或流形)上的(李)群G。对于最简单的设置之一,可以看到当基是流形时(我们称之为连续统规范理论)[1-5]。当基是简单复合体(或晶格)时,模型称为离散规范理论。它们正式地作为前几个的集成版本(或多或少正式地)。物理研究包括解P上的方程,或量子化后的场,最小化所谓的作用泛函。这些作用泛函实际上作用于连接,连接是局部切片的无穷小表达式。它们也可以理解为微分算子。在过去的二十年里,这些来自物理学的微妙设置为我们的知识找到了两个应用:在所谓的“几何套利理论”中,用于管理金融市场的投资组合[2,6-10],其中纤维束或主要束沿着其纤维和基础描述了用于预测投资组合演变的外部和内部参数。在[9]中描述的流形上的光纤束以[10]中描述的方式与主束相关。在所谓的两两比较中的近似推理中,当* G + =和具有归一化对象时,可以进行完整的研究,并具有最近的公理化[10-19]。然而,所谓的非数值排序的需要呼吁使用非阿贝尔群G和来自离散规范理论的数学工具[12,18]。这篇短文的目的是提出唯一作者的观点,即神经病学和生物学中存在一些隐藏的方面,这些方面在某种匹配镜像过程中揭示了测量效应的存在。这方面实际上并没有得到真正的证实,它或多或少处于概念化的阶段,这就是我们谈论规范幽灵的原因。其他作者已经提到了来自生物学和神经科学的模型的相似性[11,15,16],每次都对经典事实进行了非常温和的解释。从启发式的观点来看,这并不奇怪,因为“生命物理学”处理的是相互作用的实体,就像核物理学的基本粒子相互作用一样。在微观尺度上,通过将一个实体(神经元、分子、细胞)与其相邻的每个实体连接起来,一个简单的结构自然产生,这使得离散规范理论变得合适;在宏观层面上,现象的第一个近似可以从连续规范理论中产生。然而,作为最后的评论,规范理论离散化的数学理论必须受到非常严重的质疑,因为基于的经典方案导致数学问题,而其他尚未研究的方法来离散连续体模型可以提出[17]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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