Large Deviations and Extremes for Random Matrices, Tensors, and Fields

随机矩阵、张量和场的大偏差和极值

• 批准号: 2154029
• 负责人: Nicholas Cook
• 金额: $ 26万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154029&HistoricalAwards=false
• 关键词: Large; Deviations; Extremes; Random; Matrices

This project aims to address several problems in two active and related areas at the interface of probability theory and statistical physics, namely, large deviations theory (LDT) and extreme value theory. LDT is concerned with estimating the probabilities of rare events, and understanding the mechanisms by which these events arise. One of the main focuses of the project is on rare events for random networks. Random networks are large collections of nodes (or individuals) where pairs of nodes are connected at random. The project aims to describe the large-scale structure of random networks with an atypical number of instances of some small-scale pattern, such as three mutual friends. Such an understanding would have implications for statistical estimation of the structure of large social networks. The second aim of the project concerns extreme values for logarithmically correlated fields (LCFs), which arise in problems ranging from analytic number theory to mathematical ecology. The project aims to advance the understanding of universal and non-universal aspects of LCFs in the context of random matrices and reaction-diffusion systems. The project provides research training opportunities for graduate and undergraduate students. The problems concerning LDT focus on questions about nonlinear functions of random hypergraphs and random matrices. The project will further develop a recent approach to LDT for random hypergraphs based on tensor decompositions, with connections to the regularity method in extremal graph theory and the mean-field approximation in statistical physics, and with applications to the study of Gibbs measures used to model social networks. In the context of random matrices, the project will further advance a recent approach to large deviations of extremal eigenvalues through the analysis of spherical integrals, in order to address models with general entry distributions, including sparse models. The project on extreme values for LCFs aims to develop flexible tools to study a broad class of models in random matrix theory, where the strongest results to date are confined to classical ensembles with smooth symmetries. In a different direction, the project will extend a probabilistic approach to the study of reaction-diffusion equations in order to study coupled systems of partial differential equations in higher dimensions with boundary interactions, with particular attention to systems used to model the propagation of invasive species.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.

该项目旨在解决概率论和统计物理交叉领域两个活跃且相关领域的几个问题，即大偏差理论（LDT）和极值理论。 LDT 涉及估计罕见事件的概率，并了解这些事件发生的机制。该项目的主要焦点之一是随机网络的罕见事件。随机网络是节点（或个体）的大型集合，其中节点对随机连接。该项目旨在描述随机网络的大规模结构，其中包含一些小规模模式的非典型实例数量，例如三个共同的朋友。这种理解将对大型社交网络结构的统计估计产生影响。该项目的第二个目标涉及对数相关域（LCF）的极值，这些极值出现在从解析数论到数学生态学的各种问题中。该项目旨在促进对随机矩阵和反应扩散系统背景下 LCF 的普遍和非普遍方面的理解。该项目为研究生和本科生提供研究培训机会。 LDT的问题主要集中在随机超图和随机矩阵的非线性函数问题上。该项目将进一步开发一种基于张量分解的随机超图 LDT 最新方法，与极值图论中的正则方法和统计物理学中的平均场近似相联系，并应用于用于建模的吉布斯测度的研究社交网络。在随机矩阵的背景下，该项目将通过球积分分析进一步推进最近的极值特征值大偏差方法，以解决具有一般条目分布的模型，包括稀疏模型。 LCF 极值项目旨在开发灵活的工具来研究随机矩阵理论中的广泛模型，其中迄今为止最强的结果仅限于具有平滑对称性的经典系综。在另一个方向上，该项目将扩展概率方法来研究反应扩散方程，以研究具有边界相互作用的高维偏微分方程耦合系统，特别关注用于模拟入侵物种传播的系统该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。