Communications of the ACM
Researcher Seeks to Tame 'ghost' of ncertainty in Complex Dynamic Systems
Huazhen Fang, a professor at the University of Kansas, is leading research to address the "unstructured uncertainty" challenge in the quest to develop accurate predictive mathematical equations and algorithms dealing with complex dynamic systems.
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University of Kansas professor Huazhen Fang is leading research to address the "unstructured uncertainty" challenge in the quest to develop accurate predictive mathematical equations and algorithms dealing with complex dynamic systems.
"We try to identify the probability-based appearance of uncertainties conditioned on the data," Fang says. "This will lead us to develop mathematical models and efficient algorithms that can effectively account for the ghost presence of uncertainty."
Fang's efforts concern holistic investigation, which not only deals with predicting a system's behavior despite uncertainties but also analyzes sensor-based observation structure to acquire high-quality data beneficial for prediction.
Fang says the project could have many military uses, including enabling more robust autonomy, navigation, guidance, target tracking, sensor fusion, fault detection, and structural health prognostics. He also notes the basic research could power consumer products such as global-positioning system navigation by augmenting the Kalman filter, an algorithm already used to contend with uncertainty.
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