This award is funded under the American Recovery and Reinvestment Act of 2009 (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.
