Collaborative research: Gaussian Process Frameworks for Modeling and Control of Stochastic Systems

合作研究：随机系统建模和控制的高斯过程框架

基本信息

批准号：
1821258
负责人：
Robert Gramacy
金额：
$ 15万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821258&HistoricalAwards=false
关键词：
Collaborative research Gaussian Process Frameworks

项目摘要

Quantitative models for decision making under uncertainty continue to attract intense effort across natural sciences and engineering. With the advent of ever more sophisticated models in applications, computational demands continue to outpace what is feasible and the premium on efficient numerical approaches remains high. The investigators will explore synergies between the latest machine learning techniques and control paradigms, arising in applications as diverse as finance, energy storage and security, and the epidemiological modeling of infectious diseases. The developed "smart" algorithms will deliver performance upgrades essential for using simulations in tackling large-scale/complex settings. The project will also contribute to inter-disciplinary training in mathematical sciences across undergraduate, graduate and post-doctoral levels. The investigators will investigate statistical learning techniques for modeling, analysis and control of nonlinear dynamic stochastic systems. Through developing algorithms and statistical models for complex stochastic simulators, and active learning strategies for autonomous data acquisition, the project will achieve enhanced capabilities and efficiency in mathematical analysis of dynamic random phenomena. The approach hinges on the use of high fidelity approximate Gaussian Process surrogates to adaptively allocate computing resources in order to maximize the learning rate of the input-output relationship for modeling objectives or of the input-control map for dynamic programming. By connecting stochastic simulation with machine learning and non-parametric statistics, and integrating with the computational implementation, the project will enhance knowledge discovery in large-scale simulation and optimization settings.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.

在不确定性下进行决策的定量模型继续吸引自然科学和工程的巨大努力。随着应用程序中越来越复杂的模型的出现，计算需求继续超过可行的，并且有效的数值方法的溢价仍然很高。研究人员将探索最新的机器学习技术与控制范式之间的协同作用，这些应用在金融，储能和安全等多样化的应用中以及传染病的流行病学建模。开发的“智能”算法将提供对使用模拟来解决大规模/复杂设置所必需的性能升级。该项目还将为跨本科，研究生和博士后水平的数学科学进行跨学科培训。研究人员将研究非线性动态随机系统建模，分析和控制的统计学习技术。通过开发复杂随机模拟器的算法和统计模型，以及自主数据获取的主动学习策略，该项目将在动态随机现象的数学分析中实现增强的能力和效率。该方法取决于使用高保真度近似高斯流程代理，以适应分配计算资源，以最大程度地提高目标形成目标或动态编程的输入控制映射的输入输出关系的学习率。通过将随机模拟与机器学习和非参数统计数据联系起来，并与计算实现集成，该项目将在大规模的模拟和优化环境中增强知识发现。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持，并被认为是值得的。