CAREER: Large-scale Spatial Temporal Data Driven Simulation with Sequential Monte Carlo Methods

职业：使用顺序蒙特卡罗方法进行大规模时空数据驱动仿真

• 批准号: 0841170
• 负责人: Xiaolin Hu
• 金额: $ 42.52万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0841170&HistoricalAwards=false
• 关键词: CAREER; Large; scale; Spatial; Temporal

This award is funded under the American Recovery and Reinvestment Act of2009 (Public Law 111-5).Large-scale spatial temporal systems such as wildfire are inherently difficult to study due to their complex and dynamical behavior. Computer modeling and simulation provide an important tool for understanding and predicting the dynamic behavior of these systems. While sophisticated simulation models have been developed, traditional simulations are largely decoupled from real systems by making little usage of real time data from the systems under study. With recent advances in sensor and network technologies, the availability and fidelity of such real time data have greatly increased. A new paradigm of dynamic data-driven simulation is emerging where a simulation system is continually influenced by the real time data for better analysis and prediction of a system under study. This project investigates tractable approaches for dynamic data driven simulation of large-scale spatial temporal systems based on state-of-the-art probabilistic techniques using Sequential Monte Carlo (SMC) methods. New algorithms and methods are developed to enhance the effectiveness and efficiency of data driven simulation of large-scale spatial temporal systems. The project builds upon the application context of wildfire that the PI has experience with.This project will have a strong impact on both theory and practice aspects of simulation-based study of large-scale complex systems in general, and wildfire in particular. The project will result in major advances to the new paradigm of dynamic data-driven simulation, and can potentially benefit many other fields where sophisticated simulation models are used, such as manufacturing, transportation, geo-ecological science, and national security. The project also has a comprehensive education component, including course development, involving undergraduates and under-represented students in research, and international student exchange. Dissemination will include demonstrations, a shared simulation environment, and workshops/tutorials.

该奖项由2009年美国复苏和再投资法案（公法111-5）资助。大规模时空系统，如野火，由于其复杂和动态的行为，本质上是难以研究的。计算机建模和仿真为理解和预测这些系统的动态行为提供了重要的工具。 虽然已经开发了复杂的仿真模型，但是传统的仿真通过很少使用来自所研究的系统的真实的时间数据而在很大程度上与真实的系统解耦。 随着传感器和网络技术的最新进展，这种真实的时间数据的可用性和保真度已经大大增加。 一种新的动态数据驱动仿真模式正在出现，其中仿真系统不断受到真实的时间数据的影响，以便更好地分析和预测所研究的系统。 本计画以序贯蒙地卡罗方法为基础，探讨动态资料驱动模拟大规模时空系统的可行方法。 为了提高大规模时空系统数据驱动仿真的有效性和效率，提出了新的算法和方法。 该项目建立在PI具有经验的野火应用背景之上。该项目将对基于模拟的大规模复杂系统研究的理论和实践方面产生重大影响，特别是野火。 该项目将导致动态数据驱动仿真新范式的重大进展，并可能使许多其他使用复杂仿真模型的领域受益，如制造业，交通运输，地理生态科学和国家安全。 该项目还有一个全面的教育组成部分，包括课程编制，让本科生和代表人数不足的学生参与研究，以及国际学生交流。 传播将包括演示、共享模拟环境和研讨会/教程。