On Rough Differential Systems and Stochastic Analysis

粗微分系统与随机分析

• 批准号: 1613163
• 负责人: Samy Tindel
• 金额: $ 20.72万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613163&HistoricalAwards=false
• 关键词: Rough; Differential; Systems; Stochastic; Analysis

Stochastic calculus is a branch of probability theory that aims to study and interpret differential systems with a noisy input (also called stochastic differential equations). The most common example of such noisy input is Brownian motion, which occurs in models throughout science, engineering, and economics. Within this context, the so-called rough paths theory is a powerful method that allows one to define noisy systems in a wide variety of situations (well beyond the Brownian motion case). The rough paths theory also gives an almost-deterministic point of view on stochastic calculus, as opposed to the traditional approach, which is highly probabilistic in essence. The term stochastic analysis usually encompasses both stochastic integration of Itô type and Malliavin calculus techniques. The latter can be viewed as a way to define an analysis at the path level; it leads to deep and useful results concerning stochastic differential equations. This research project aims to combine rough-paths and Malliavin calculus techniques in order to give a meaning to and then study noisy partial differential equations that model heat transfer in random environments. The project will also study the large time behavior of differential equations driven by a general class of noises, and see how to statistically identify this kind of system by observing a typical path. This research project focuses on interactions between rough paths theory, stochastic partial differential equations (PDE), and Malliavin calculus. A more specific list of the six subprojects can be structured as follows: (1) stochastic PDEs, with (a) parabolic Anderson model in rough environment, (b) parabolic Anderson model in dimension 2, and (c) density for solutions to rough PDEs; and (2) rough finite-dimensional systems, with (a) ergodic properties for rough differential equations, (b) estimation procedures for rough stochastic differential equations, and (c) renormalization of numerical schemes for rough stochastic differential equations. In all those projects, the principal investigator will use stochastic analysis methods combined with analysis and rough paths tools in order to study some new yet very natural classes of processes. Because even the definition of those objects was far from clear before the rough path theory was introduced, their study is both motivating and challenging.

随机微积分是概率论的一个分支，旨在研究和解释具有噪声输入的微分系统（也称为随机微分方程）。这种噪声输入最常见的例子是布朗运动，它出现在科学、工程和经济学的模型中。在这种背景下，所谓的粗糙路径理论是一种强大的方法，它允许人们在各种情况下定义噪声系统（远远超出布朗运动的情况）。粗糙路径理论也给出了一个几乎确定性的观点随机微积分，而不是传统的方法，这是高度概率的本质。术语随机分析通常包括伊藤型随机积分和Malliavin演算技术。后者可以被看作是一种方法来定义一个分析的路径水平，它导致深入和有用的结果有关随机微分方程。本研究计划旨在结合联合收割机粗糙路径和Malliavin演算技术，以赋予意义，然后研究噪声偏微分方程，模型在随机环境中的热传递。本研究项目还将研究由一般类型的噪声驱动的微分方程的大时间行为，以及如何通过观察典型路径来统计识别这类系统。本研究项目的重点是粗糙路径理论、随机偏微分方程（PDE）和Malliavin演算之间的相互作用。这六个子项目的更具体的列表可以构造如下：（1）随机偏微分方程，（a）粗糙环境中的抛物型安德森模型，（B）二维抛物型安德森模型，和（c）粗糙偏微分方程解的密度;（2）粗糙有限维系统，（a）粗糙微分方程的遍历性，（B）粗糙随机微分方程的估计过程，粗糙随机微分方程数值格式的重整化。 在所有这些项目中，首席研究员将使用随机分析方法结合分析和粗糙路径工具，以研究一些新的但非常自然的过程类别。因为在粗糙路径理论被引入之前，甚至这些对象的定义也远不清楚，所以他们的研究既有动力又有挑战性。