Asymptotic problems for stochastic partial differential equations

随机偏微分方程的渐近问题

• 批准号: 1407615
• 负责人: Sandra Cerrai
• 金额: $ 32.1万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1407615&HistoricalAwards=false
• 关键词: Asymptotic; problems; stochastic; partial; differential

The objective of this research project is the development of new methods for the analysis of systems described by partial differential equations, in the presence of a noisy (random) perturbation and small parameters. We are interested in the description of the different behaviors of such systems, when the parameters are vanishing, and of the interplay between different limiting regimes. In particular, we will study new mathematical problems which are important for applications, as well as new effects in classical problems. The treatment of these problems for systems with an infinite number of degrees of freedom is a relatively new field of investigation, which has already stirred up a vivid interest in many fields, also because these are very complex objects and any effort that goes in the direction of their simplification is important for a deeper understanding of the main features of the models and for a better effectiveness in applications. Our analysis requires the development of new methods and the substantial introduction of new techniques which have to range over many fields in mathematics. Our goal in this proposal is studying small deterministic and stochastic perturbations of a wide class of systems described by stochastic partial differential equations. As a matter of fact, small perturbations, which are negligible on one time scale, can become crucial on a larger time scale. The long-time influence of small perturbations has been considered in a number of our previous papers, and the present project has to be considered as a continuation of this program. Limit theorems, especially the large deviation theory, the averaging principle and the interplay between them, as well as several generalizations of the Smoluchowskii-Kramers approximation are our main tools. Systems with many/infinite degrees of freedom often have perturbations of different origin and different order. Long-time behavior of such perturbed systems should be described by a hierarchy of approximations. On the other hand, long-time behavior of pure deterministic systems with instabilities, under certain conditions, should be described by a stochastic process. Therefore, the natural generality for the problem is in considering both deterministic and stochastic perturbations of stochastic systems (not necessarily deterministic dynamical systems).

本研究项目的目标是开发新的方法，用于分析由偏微分方程描述的系统，在存在噪声（随机）扰动和小参数的情况下。我们感兴趣的描述不同的行为，这样的系统，当参数消失，和不同的限制制度之间的相互作用。特别是，我们将研究新的数学问题，这是重要的应用，以及在经典问题的新效果。对具有无限多个自由度的系统的这些问题的处理是一个相对较新的研究领域，它已经在许多领域引起了强烈的兴趣，也因为这些是非常复杂的对象，并且任何朝着简化方向的努力对于更深入地理解模型的主要特征和更好的应用效果都是重要的。我们的分析需要发展新的方法和大量引进新的技术，这些技术必须涉及数学的许多领域。在这个建议中，我们的目标是研究小的确定性和随机扰动的一类随机偏微分方程描述的系统。事实上，在一个时间尺度上可以忽略不计的小扰动在更大的时间尺度上可能变得至关重要。小扰动的长期影响在我们以前的一些论文中已经被考虑过，本项目必须被认为是这个计划的延续。极限定理，特别是大偏差理论，平均原理和它们之间的相互作用，以及几个推广的Smoluchowskiii-Kramers近似是我们的主要工具。具有多个/无限自由度的系统通常具有不同来源和不同阶的扰动。这种扰动系统的长时间行为应该用一系列近似来描述。另一方面，具有不稳定性的纯确定性系统的长期行为，在一定条件下，应该用随机过程来描述。因此，自然的一般性问题是在考虑确定性和随机扰动的随机系统（不一定是确定性的动力系统）。