Quantitative, Computational, and Stochastic Aspects of Topology

拓扑的定量、计算和随机方面

• 批准号: 1906516
• 负责人: Fedor Manin
• 金额: $ 17.42万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1906516&HistoricalAwards=false
• 关键词: Quantitative; Computational; Stochastic; Aspects; Topology

Topology is the study of global properties of geometric objects which are preserved under deformation; it has recently found applications in areas such as protein folding and analysis of high-dimensional data. A particularly successful approach, which has produced innumerable results since the 1950's, is computing algebraic invariants which are then studied through algebraic means. However, in many situations this way of transforming the problem hides some inherent geometric complexity -- for example, one can deform one object to another, but only by making it very complicated somewhere in the middle. In such a case, the existence of a deformation may not be particularly meaningful from a physical, application-oriented point of view. In other cases, in contrast, one can always find a reasonably straightforward deformation, validating the use of algebraic methods for applications. The purpose of this project is to investigate these phenomena.The project will enrich our understanding of the ideas of geometric topology by proving results of three types: quantitative results, measuring the size and complexity, in various senses, of objects whose existence is known via algebra; algorithmic results, showing that certain problems can be resolved algorithmically while others cannot; and stochastic results, describing the properties of random objects. One can think of many such results as answering questions about the geometry of function and moduli spaces. Closely related questions are studied in geometric group theory, knot theory, the combinatorics of expander graphs, and the theory of topological data analysis. However, the study of high-dimensional and simply connected manifolds and complexes requires a different set of tools which have only begun to be developed. Since function spaces are found all over mathematics, this project offers connections to a number of mathematical fields ranging from theoretical computer science to fluid dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

拓扑学是研究几何对象在变形下保持的全局性质的学科;它最近在蛋白质折叠和高维数据分析等领域得到了应用。一个特别成功的方法，这已经产生了无数的结果，自20世纪50年代以来，是计算代数不变量，然后通过研究代数手段。 然而，在许多情况下，这种转换问题的方式隐藏了一些固有的几何复杂性-例如，人们可以将一个对象变形为另一个对象，但只能在中间的某个地方使其变得非常复杂。 在这种情况下，从物理的、面向应用的观点来看，变形的存在可能不是特别有意义的。 相反，在其他情况下，人们总是可以找到一个相当简单的变形，验证使用代数方法的应用。 这个项目的目的是研究这些现象。这个项目将通过证明三种类型的结果来丰富我们对几何拓扑思想的理解：定量结果，测量物体的大小和复杂性，在不同的意义上，这些物体的存在是通过代数已知的;算法结果，表明某些问题可以通过算法解决，而其他问题则不能。和随机结果，描述随机对象的属性。人们可以认为许多这样的结果回答问题的几何函数和模空间。在几何群论、纽结理论、扩张图的组合学和拓扑数据分析理论中研究了密切相关的问题。然而，研究高维和单连通流形和复形需要一套不同的工具，这些工具才刚刚开始发展。由于函数空间在数学中随处可见，因此该项目提供了从理论计算机科学到流体动力学的许多数学领域的联系。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。