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

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。像野火这样的大尺度时空系统，由于其复杂和动态的行为，天生就很难研究。计算机建模与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，但传统的仿真在很大程度上是通过很少使用来自所研究系统的实时数据而与真实系统解耦的。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大大提高。一种新的动态数据驱动的仿真范式正在出现，其中仿真系统不断地受到实时数据的影响，以更好地分析和预测所研究的系统。该项目基于最先进的概率技术，使用序列蒙特卡罗(SMC)方法，研究了大规模时空系统的动态数据驱动模拟的易处理方法。为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对野火的应用背景之上，该项目将对一般大型复杂系统，特别是野火的基于仿真的研究的理论和实践方面产生强烈的影响。该项目将导致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，如制造业、交通运输、地球生态科学和国家安全。该项目还包括一个全面的教育部分，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。

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.