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Intrinsic Complexity of Random Fields and Its Connections to Random Matrices and Stochastic Differential Equations

随机场的内在复杂性及其与随机矩阵和随机微分方程的联系

基本信息

批准号：
2048877
负责人：
Hong-Kai Zhao
金额：
$ 2.6万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048877&HistoricalAwards=false
关键词：
Intrinsic Complexity Random Fields Its

项目摘要

Scientific computing together with effective use of data plays a more and more important role in many science and engineering applications. Modeling and understanding uncertainty or randomness inherited in these application is of utmost importance. Random fields are commonly used for modeling of space (or time) dependent stochastic processes in science and engineering problems, such as image and signal processing, Bayesian inference, data analysis, uncertainty quantification, and many other applications. It has the flexibility and generality to model randomness with spatial structures or vice versa. This project will characterize the intrinsic complexity of a random field.For the purpose of analysis as well as modeling and computation in practice, a separable representation or approximation of a random field in the form of separating deterministic and stochastic variables is very useful. This project will characterize the intrinsic complexity of a random field by providing accurate and computable lower bounds on the number of terms needed in a separable approximation of a random field for a given accuracy. This characterization can be related to the well-known notion of Kolmogorov n-width in information theory. It can reveal the intrinsic degrees of freedom (or richness) of a random field. It is also useful for an estimation of the intrinsic complexity of a system that is modeled upon a random field in real applications. For example, the investigator will study the intrinsic complexity of the solution space for partial differential equations that involve random material properties and develop efficient numerical methods that can explore low dimensional structures in these systems. By regarding a set of random vectors as the discrete sampling of a random field and vice versa, the investigator will also study the question of random vector embedding and explore its connections to random matrix theories.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.

科学计算与数据的有效利用在许多科学和工程应用中发挥着越来越重要的作用。建模和理解这些应用中继承的不确定性或随机性是至关重要的。随机场通常用于建模空间（或时间）依赖的随机过程在科学和工程问题，如图像和信号处理，贝叶斯推理，数据分析，不确定性量化，和许多其他应用。它具有灵活性和通用性，可以对空间结构的随机性进行建模，反之亦然。该项目将描述随机场的内在复杂性，为了分析以及实际建模和计算的目的，以分离确定性和随机变量的形式对随机场进行可分离的表示或近似是非常有用的。这个项目将通过提供一个给定精度的随机场的可分离近似所需的项的数量的精确和可计算的下限来表征随机场的内在复杂性。这个特征可以与信息论中著名的Kolmogorov n宽度概念联系起来。它可以揭示随机场的内在自由度（或丰富度）。它也是有用的估计固有的复杂性的一个系统，在真实的应用程序的随机场建模。例如，研究人员将研究涉及随机材料特性的偏微分方程的解空间的内在复杂性，并开发有效的数值方法，可以探索这些系统中的低维结构。通过将一组随机向量视为随机场的离散采样，反之亦然，研究人员还将研究随机向量嵌入问题，并探索其与随机矩阵理论的联系。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。