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Probabilistic Methods in Analysis, Geometry, and Beyond

分析、几何及其他领域的概率方法

基本信息

批准号：
1954264
负责人：
Maria Gordina
金额：
$ 33万
依托单位：
University of Connecticut
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1954264&HistoricalAwards=false
关键词：
Probabilistic Methods Analysis Geometry Beyond

项目摘要

This project develops several research directions combining probability, geometry and analysis, with many problems motivated by physics. The main object of study are random systems with a certain level of degeneracy similar to a constrained movement. Such degenerate diffusions in high or infinite dimensions have many applications in different fields including quantum field theory (QFT), turbulence, chemical dynamics, and large data environments. While they are useful in modelling many of such phenomena, degenerate and high-dimensional nature of the setting pose mathematical challenges. A number of the projects are for graduate and possibly undergraduate students. In addition, the results will be used for mentoring and educational activities at the local middle school and at the undergraduate level.One of the directions of research is to study Cameron-Martin-Girsanov type quasi-invariance in hypoelliptic settings, and its applications to functional inequalities, smoothness of probability laws in subelliptic and singular settings. This is closely related to asymptotic behavior of such diffusions, namely, large deviations, the Onsager–Machlup functional which can be viewed as an analog of the Lagrangian of a dynamical system, and convergence to equilibrium of a large particle system with singular potentials. Both degeneracy (lack of ellipticity) and high dimensions have to be dealt with new techniques coming from different fields such as probability, ergodic theory and sub-Riemannian geometry. While many of these settings arise naturally in applications, their mathematical analysis is not easy. In addition to theoretical significance of such questions, some answers have practical uses. For example, the rate of convergence to the equilibrium, its dependence on the number of particles and other parameters, or an explicit form of the rate function have many applications.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.

这个项目发展了几个结合概率、几何和分析的研究方向，有许多问题是由物理驱动的。主要研究对象是具有一定退化程度的随机系统，类似于受约束的运动。这种高维或无限维简并扩散在量子场论、湍流、化学动力学和大数据环境等领域有着广泛的应用。虽然它们在模拟许多这样的现象时很有用，但环境的退化和高维性质构成了数学上的挑战。许多项目是为研究生和可能的本科生准备的。研究方向之一是研究次椭圆环境下的Cameron-Martin-Girsanov型拟不变性及其在次椭圆和奇异环境下的泛函不等式、概率律的光滑性等方面的应用。这与这种扩散的渐近行为密切相关，即大偏差、可视为动力系统拉格朗日模拟的Onsager-Machlup泛函以及具有奇异势的大粒子系统的收敛到平衡。无论是退化(缺乏椭圆性)还是高维，都必须用来自不同领域的新技术来处理，如概率、遍历理论和次黎曼几何。虽然这些设置中的许多都是在应用程序中自然产生的，但它们的数学分析并不容易。除了这些问题的理论意义外，一些答案还具有实际用途。例如，收敛到平衡的速度，它对粒子数量和其他参数的依赖，或速率函数的显式形式有许多应用。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。