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Modeling, Analysis, Optimization, Computation, and Applications of Stochastic Systems

随机系统的建模、分析、优化、计算和应用

基本信息

批准号：
2204240
负责人：
Gang George Yin
金额：
$ 61.5万
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：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204240&HistoricalAwards=false
关键词：
Modeling Analysis Optimization Computation Applications

项目摘要

This project aims to study stochastic systems in which random disturbances play a significant role. This research will encompass the study of the dynamic behavior of mathematical models and applications in areas of ecological and biological systems, wireless communication, financial engineering, networked systems, and systems in control engineering. The research will focus on model systems under the random influence, switching among different configurations, and complex structures. The results will provide an understanding of the fundamental properties and the basic features of such modeling systems. This project will provide training opportunities for graduate and undergraduate students. The research will promote diversity and inclusion, increase public scientific literacy, and enhance interdisciplinary collaborations and the STEM workforce.This project will encompass analysis and computation of several important topics from emerging and existing applications in networked systems, control engineering, optimization of systems, wireless communications, biology, ecology, economics, and social networks. (1) It aims to develop a new methodology for analyzing switching jump-diffusion type Kolmogorov systems. Novel features to be studied include non-local behavior due to the jumps, and uncertain environment modeling using random switching. Long-standing fundamental issues such as minimal conditions needed for persistence and extinction in population dynamics will be addressed. (2) Treating discontinuity in the iterates and non-smooth dynamics in the limits for stochastic approximation algorithms is vitally important. This project will focus on this issue from a new angle. Stochastic differential inclusion limits will be obtained and used to ascertain rates of convergence and to improve asymptotic efficiency for the first time. (3) Although nonlinear filtering is an area deemed to be well developed, computation remains to be the main challenge because of the infinite dimensionality. This project aims to develop a methodology based on machine learning and neural networks with a new approach using adaptive learning rate recursion, leading to potentially more efficient computational methods. (4) A key in numerically solving nonlinear stochastic differential equations is to treat high nonlinearity and numerical finite time explosion. This project will develop a class of algorithms to handle the problem. A novel idea will be the use of randomly generated growing truncation bounds. Convergence and rates of convergence will be developed. (5) In response to the urgent need to handle coupled equations in networks, this project will focus on the study of coupled switching jump diffusions. By using ideas from dynamic systems and coupling methods in probability, this project aims to obtain stability and stabilization with impact on networked systems. Extensive numerical experiments and simulations will be performed to complement the mathematical analysis and algorithm design. It will open a new domain for further research in mathematics with a broader range of 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.

本计画的目的是研究随机系统，其中随机干扰扮演重要的角色。这项研究将包括数学模型的动态行为和应用在生态和生物系统，无线通信，金融工程，网络系统和控制工程系统领域的研究。研究将集中在随机影响下的模型系统，不同配置之间的切换，以及复杂的结构。结果将提供这样的建模系统的基本属性和基本特征的理解。该项目将为研究生和本科生提供培训机会。该研究将促进多样性和包容性，提高公众的科学素养，加强跨学科合作和STEM劳动力。该项目将包括分析和计算网络系统，控制工程，系统优化，无线通信，生物学，生态学，经济学和社交网络中新兴和现有应用的几个重要主题。(1)它旨在发展一种新的方法来分析切换跳跃扩散型Kolmogorov系统。新的功能进行研究，包括非本地的行为，由于跳跃，和不确定的环境建模使用随机切换。将讨论长期存在的基本问题，如种群动态持续存在和灭绝所需的最低条件。(2)处理迭代过程中的不连续性和极限中的非光滑动力学是随机逼近算法的重要内容。本项目将从一个新的角度来关注这一问题。随机微分包含极限将获得和使用，以确定收敛速度和提高渐近效率的第一次。(3)虽然非线性滤波被认为是一个发展良好的领域，但由于无限维，计算仍然是主要的挑战。该项目旨在开发一种基于机器学习和神经网络的方法，采用自适应学习率递归的新方法，从而可能产生更有效的计算方法。(4)数值求解非线性随机微分方程的一个关键是处理强非线性和数值有限时间爆炸。这个项目将开发一类算法来处理这个问题。一个新的想法将是使用随机生成的增长截断界限。收敛和收敛速度将得到发展。(5)为因应网路中耦合方程式处理的迫切需求，本计画将集中研究耦合切换跳跃扩散。本计画利用动态系统的思想与机率中的耦合方法，以网路系统为研究对象，探讨其稳定性与冲击稳定性。将进行大量的数值实验和模拟，以补充数学分析和算法设计。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。