9月15日 Bayesian Probability for Sensor Fusion and Pattern Recognition in Fusion Devices

报告题目：Bayesian Probability for Sensor Fusion and Pattern Recognition in Fusion Devices

报告时间：9月15日（星期一）上午9:30-11:30

报告地点：四号楼601会议室

报告人：Prof. Geert Verdoolaege

主持人：张洋 研究员

报告人简介：Prof. Geert Verdoolaege is from the Ghent University. He obtained the MSc degree in Theoretical Physics in 1999 and the PhD in Engineering Physics in 2006, both at Ghent University. His research activities comprise development of data analysis techniques using methods from probability theory, machine learning and information geometry, and their application to nuclear fusion experiments.

报告简介：

Fusion energy research can benefit greatly from modern data science methods, both for increasing the understanding of the underlying plasma physics and for optimizing the design and operation of fusion devices. From basic statistical techniques for model fitting, to Bayesian methods for probabilistic analysis of data from single or multiple diagnostics, to the latest machine learning techniques for anomaly detection and uncertainty quantification: the applications are numerous and the possible approaches originate from a broad range of subfields of the information sciences. In this talk, I will highlight a number of recent applications of Bayesian inference in fusion. I will start with sensor fusion, i.e. the systematic, joint treatment of data from multiple, heterogeneous diagnostics. In particular, Bayesian probability is seen to gracefully handle tomographic inversion using Gaussian process models. Opportunities for diagnostic design optimization and machine learning techniques for speeding up the inference process are also touched upon, with a view to real-time sensor fusion in future devices. I then proceed to applications of Bayesian inference and information geometry for pattern recognition in fusion data, concentrating on robust parameter estimation in complex, multi-machine data sets, as well as anomaly detection for predictive maintenance in fusion devices.