Investigation of Interacting Particle Systems by Stochastic Analysis Methods

用随机分析方法研究相互作用的粒子系统

• 批准号: 1506290
• 负责人: Mykhaylo Shkolnikov
• 金额: $ 17.36万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1506290&HistoricalAwards=false
• 关键词: Investigation; Interacting; Particle; Systems; Stochastic

Interacting particle systems originate from mathematical physics, but are now used in a number of areas in applied mathematics. Among others, they can be used to model phenomena such as capital distribution in equity markets, fluid dynamics, stability of queueing networks, and traffic flow. The main goal of this project is to obtain a better mathematical understanding of interacting particle systems and to relate them to other mathematical objects, including partial differential equations, random matrices, random polymers, random Young tableaux, and symmetric functions. Such detailed mathematical analysis would provide new information on the applicability of interacting particle systems as mathematical models within the fields described above, as well as on their limitations. In addition, with the mathematics of interacting particle systems being on the interface of probability theory, partial differential equations, and representation theory, the proposed research is expected to unveil connections between these areas of mathematics. Such connections are of great interest as they allow one to combine tools from all these different areas in future research.More specifically, the PI intends to study interacting particle systems with continuous state spaces in which the particle dynamics can be described by interacting Brownian (or, more generally, diffusive) motions. Typically, such systems are the continuous analogues of the more classical discrete interacting particle systems. However, the continuity of the state space allows one to apply methods of stochastic analysis to such systems and is natural due to the continuous nature of the (stochastic) partial differential equations describing the macroscopic behavior of interacting particle systems. In particular, the PI intends to study systems in the Kardar-Parisi-Zhang universality class, which describes the microscopic dynamics behind the growth of random surfaces. Examples of such systems include, among others, the Brownian totally asymmetric simple exclusion process and the O'Connell-Yor semi-discrete polymer. In this context one is particularly interested in the asymptotic behavior of the leading particles and the spacings between them, and their investigation is a major part of the proposed research.

相互作用的粒子系统起源于数学物理学，但现在在应用数学的许多领域中使用。除其他外，它们可用于模拟股票市场中的资本分配、流体动力学、网络稳定性和交通流量等现象。该项目的主要目标是获得相互作用粒子系统的更好的数学理解，并将其与其他数学对象，包括偏微分方程，随机矩阵，随机聚合物，随机Young tableaux和对称函数。这种详细的数学分析将提供关于相互作用粒子系统作为上述领域内的数学模型的适用性以及它们的局限性的新信息。此外，随着相互作用粒子系统的数学处于概率论，偏微分方程和表示论的界面上，预计拟议的研究将揭示这些数学领域之间的联系。这样的连接是非常有趣的，因为它们允许一个联合收割机工具，从所有这些不同的领域在未来的research.More具体地说，PI打算研究相互作用的粒子系统与连续的状态空间中的粒子动力学可以描述的相互作用布朗（或更一般地说，扩散）运动。通常，这样的系统是更经典的离散相互作用粒子系统的连续模拟。然而，状态空间的连续性允许将随机分析的方法应用于这样的系统，并且由于描述相互作用的粒子系统的宏观行为的（随机）偏微分方程的连续性，这是自然的。特别是，PI打算研究Kardar-Parisi-Zhang普适类中的系统，该普适类描述了随机表面生长背后的微观动力学。这种系统的实例包括布朗完全不对称简单排阻过程和O 'Connell-Yor半离散聚合物等。在这种情况下，人们对领先粒子的渐近行为和它们之间的间距特别感兴趣，并且它们的调查是拟议研究的主要部分。