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FRG: Collaborative Research: Integrable Probability

FRG：协作研究：可积概率

基本信息

批准号：
1664531
负责人：
Jinho Baik
金额：
$ 26.53万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664531&HistoricalAwards=false
关键词：
FRG Collaborative Research Integrable Probability

项目摘要

Much of modern probability research seeks to understand the behavior of large and complex random systems (for instance, growth in disordered media, cracking, turbulent fluids, or traffic flow) with an aim towards developing theories with predictive and statistical value. While one can try to directly model such systems on computers, their size and complexity often render such attempts fruitless. Instead, one can look for models of such systems that are complex enough to display all of the phenomena under study, yet simple enough to admit exact mathematical computation to probe that behavior. Integrable probability is the theory behind discovering and subsequently analyzing such models. This project seeks to unify the area and various recent breakthroughs and in so doing discover a host of new types of integrable probability systems, new tools for their analysis, and new large-scale universal phenomena.Integrable probability is an area of research at the interface between probability, mathematical physics, and statistical physics on the one hand, and representation theory and integrable systems on the other. Integrable probabilistic systems are characterized by two properties: It is possible to write down concise and exact formulas for expectations of a variety of interesting observables of the systems; and asymptotics of the systems, observables, and formulas provide access to exact descriptions of new phenomena and universality classes (containing more than just integrable examples). The discovery and analysis of integrable probabilistic systems hinges upon underlying algebraic structure. These integrable probabilistic systems can be viewed as projections of powerful objects whose origins lie in representation theory and integrable systems. There is a rich history of major breakthroughs in the study of integrable probabilistic systems, including the six-vertex model, Ising model, and more recently certain models in the KPZ universality class. The basic mechanisms at the heart of many of these existing results are Schur / Macdonald processes (built off the structure of symmetric polynomials) and quantum integrable systems (built off solutions to the Yang-Baxter equation and the Bethe ansatz). Each mechanism has produced breakthrough results, such as the recent resolution of the 25-year-old physics conjecture that the KPZ stochastic partial differential equation is in the KPZ universality class. Until recently, these two routes to integrable probability have existed relatively separately. The goal of the proposed project is to create a unified theory of integrable probability, combining and generalizing the methods of Schur / Macdonald processes and quantum integrable systems and, complementarily, extracting new analyzable models and uncovering new probabilistic or physical phenomena.

许多现代概率研究都试图了解大型复杂随机系统的行为（例如，无序介质中的增长，裂缝，湍流或交通流量），旨在发展具有预测和统计价值的理论。虽然人们可以尝试在计算机上直接建模这样的系统，但它们的大小和复杂性往往使这种尝试毫无结果。相反，人们可以寻找这样的系统的模型，这些模型足够复杂，可以显示研究中的所有现象，但又足够简单，可以通过精确的数学计算来探测这种行为。可积概率是发现和随后分析这些模型的理论。该项目旨在统一该领域和各种最近的突破，并在这样做发现了一系列新类型的可积概率系统，新的工具，他们的分析，和新的大规模的普遍现象。可积概率是一个研究领域之间的接口概率，数学物理和统计物理一方面，表示论和可积系统的另一方面。可积概率系统有两个特性：可以写出系统中各种有趣的可观测量的期望值的简洁而精确的公式;系统、可观测量和公式的渐近性提供了对新现象和普适性类（包含的不仅仅是可积的例子）的精确描述。可积概率系统的发现和分析依赖于其基本的代数结构。这些可积概率系统可以被看作是起源于表示论和可积系统的强大对象的投影。在可积概率系统的研究中有着丰富的历史，包括六顶点模型，伊辛模型，以及最近在KPZ普适类中的某些模型。许多现有结果的核心基本机制是Schur / Macdonald过程（建立在对称多项式的结构上）和量子可积系统（建立在Yang-Baxter方程和Bethe不等式的解上）。每个机制都产生了突破性的结果，例如最近解决了25岁的物理学猜想，即KPZ随机偏微分方程在KPZ普适类中。直到最近，这两种途径的可积概率已经存在相对独立。该项目的目标是建立一个统一的可积概率理论，结合和推广Schur / Macdonald过程和量子可积系统的方法，并补充提取新的可分析模型，揭示新的概率或物理现象。