Feng Gu

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. We investigate 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.

1. Bai, F., Gu, F., Hu, X., and Guo, S. Particle routing in distributed particle filters for large-scale spatial temporal systems, IEEE Transactions on Parallel and Distributed Systems, 27(2): 481-493, IEEE, 2016.
2. Zhang, X., Huang, L., Ferguson-Hull, E., and Gu, F. Adaptive particle routing in parallel/distributed particle filter, HPC 2017, 2017 Spring Simulation Multi-Conference, 580-589, 2017.

Improve the performance of data assimilation of large-scale spatial temporal systems using sequential Monte Carlo methods.