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

Computer modeling and simulation provide an important tool for understanding and predicting the dynamic behavior of large-scale spatial temporal systems such as wildfire. 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. It develops new SMC-based algorithms and computing methods to enhance the effectiveness and efficiency of data driven simulation of large-scale spatial temporal systems.

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