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.

该奖项是根据 2009 年美国复苏和再投资法案（公法 111-5）资助的。像野火这样的大规模时空系统由于其复杂和动态的行为而本质上很难研究。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工具。 尽管已经开发出复杂的仿真模型，但传统仿真在很大程度上与真实系统脱钩，很少使用所研究系统的实时数据。 随着传感器和网络技术的最新进展，此类实时数据的可用性和保真度已大大提高。 动态数据驱动仿真的新范式正在出现，其中仿真系统不断受到实时数据的影响，以便更好地分析和预测所研究的系统。 该项目研究基于使用顺序蒙特卡罗（SMC）方法的最先进的概率技术的大规模时空系统的动态数据驱动模拟的易处理方法。 开发新的算法和方法来提高大规模时空系统的数据驱动模拟的有效性和效率。 该项目建立在 PI 具有经验的野火应用背景之上。该项目将对大规模复杂系统（特别是野火）的基于模拟的研究的理论和实践方面产生重大影响。 该项目将带来动态数据驱动模拟新范式的重大进展，并可能使使用复杂模拟模型的许多其他领域受益，例如制造、交通、地球生态科学和国家安全。 该项目还具有全面的教育内容，包括课程开发、让本科生和代表性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。