Current Research
Large-scale Spatial Temporal Data Driven Simulation with Sequential Monte Carlo Methods, NSF CAREER Award CNS-0841170, 2009-2014
Integrated Weather and Wildfire Simulation and Optimization for Wildfire Management, NSF ATM-0941432 (2009-2013). Xiaolin Hu (Leading PI), Collaborators: Lewis Ntaimo (Texas A&M), Ming Xue (OU), Yang Hong (OU), James Nutaro (Oak Ridge National Lab)
Agents and Multi-Agent Systems' Adaptability & Social Behavior, GSU Brain and Behavior Grant, Faculty Mentoring Grant, and P20 Grant. Collaborators: Donald. Edwards (Biology Department), 2004-2008
Research Description
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.
Integrated Weather and Wildfire Simulation and Optimization for Wildfire Management
The complexity of wildfire management arises from the uncertain dynamic interactions and dependencies among multiple system components. These include highly dynamic and nonlinear wildfire behaviors, weather conditions, and firefighting resource management. In previous research, these components were largely treated in isolation in their own fields. To achieve effective wildfire management, decision-making support tools that integrate all these components as a whole are needed. This project develops new models and computation methods that integrate weather prediction, wildfire simulation, data assimilation and stochastic optimization for effective wildfire response management. The project includes 1) coupled weather and wildfire modeling and data assimilation for two-way interactive dynamic weather-wildfire prediction, and 2) Integrated wildfire simulation and stochastic optimization for wildfire containment. It also involves parallel/distributed computational methods for fast and robust weather and wildfire behavior predictions.
Agents and Multi-Agent System's Adaptability & Social Behavior
This research develops models and simulations for studying agents and multi-agent systems' adaptability and social behavior. The research was originated in the context of animal behavior, specifically the adaptive behavior of crayfish. It proceeds to include applications in simulation of autonomous agents, mobile robots, artificial agent society, and social behavior simulation such as crowd behavior simulation. A simulation environment, BehaviorSim, is developed (Figure 1). A mobile robot test bed is set up (Figure 2). Several projects are under development based on the simulation environment and the robot test bed.
Figure 1: the BehaviorSim environment Figure 2: A mobile robot test bed
Previous Research
Wildfire Spread and Firefighting Modeling, Simulation, and Optimization, NSF CNS-0540000 (2005-2008), CNS-0720675 (2007-2009). Collaborators: Lewis Ntaimo (Texas A&M), James Nutaro (Oak Ridge National Lab)
Wildfire Spread and Firefighting Modeling, Simulation, and Optimization
This research develops an integrated modeling, simulation, and optimization system for supporting wildfire containment. This system integrates fast simulation of fire spread to predict fire behavior, just in-time optimization to compute firefighting resource dispatch plans, and modeling and simulation of firefighting to assess strategies of fire suppression. The research involves multidisciplinary team of investigators whose areas of research include discrete event modeling and simulation, stochastic programming, high performance computing, systems software, and wildland fire behavior. The two figures and two movies below show two snapshots in wildfire spread simulation and suppression simulation respectively.
Figure 1: A fire spread simulation Figure 2: A firefighting agent in action
Movies: fire spread simulation (AVI movie, 3.9M bytes), fire suppression simulation (AVI movie, 3.2M bytes)
Progressive Simulation-Based Design Framework
Some movies can be found here.