报告题目：Bayesian Estimation and Applications in Nanotechnology and Tomography

报告时间：3月19日（周二）上午10：30

报告地点：学科3号楼S410会议室

报告人：Clemens Heitzinger博士

主持人：刘青山 教授

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江苏省大数据分析技术重点实验室

江苏省气象能源利用与控制工程技术研究中心

江苏省大气环境与装备技术协同创新中心

自动化学院

2019年3月19日

报告摘要：Bayesian estimation is a method to learn unknown features from data. In particular, we use computational Bayesian inversion based on PDE (partial differential equation) models as a machine-learning method in order to identify unknown parameters which correspond to physical or geometrical properties in various applications in nanotechnological sensors and in tomography.

Applications such as electrical-impedance tomography, nanoelectrode sensors, and nanowire field-effect sensors lead to deterministic and stochastic partial differential equations that model electrostatics and charge transport in these devices.  The main model equations are the nonlinear Poisson-Boltzmann equation and the stochastic drift-diffusion-Poisson-Boltzmann system.

The main question how as much information as possible can be extracted from measurements naturally arises next.  We use computational Bayesian inversion to reconstruct physical and geometric parameters of the body interior in electrical-impedance tomography, of nanoelectrodes and the liquid in nanoelectrode sensors, and of nanowires and target molecules in nanowire field-effect sensors. The main advantage of computational Bayesian inversion is that it not only yields the unknown parameters whenever possible, but also their probability distributions and hence the uncertainties in the reconstructions, which is essential in the case of ill-posed inverse problems. In addition to showing the well-posedness of the Bayesian inversion problem for the nonlinear Poisson-Boltzmann equation, the numerical methods are presented and numerical results for the three applications such as multifrequency reconstruction for nanoelectrode sensors are shown.

报告人简介：Heitzinger博士自2015年起，在奥地利维也纳科技大学数学与地理信息学院担任副教授，同时在美国亚利桑那州立大学数学与统计学院担任兼职教授。Heitzinger曾是美国亚利桑那州立大学访问学者，美国普渡大学电子与计算机工程学院副研究员，英国剑桥大学应用数学与理论物理学院高级副研究员。2013年Heitzinger荣获由奥地利科学基金委颁发的START奖。该奖是奥地利科学院科学基金委用于表彰青年科学家的最高成就奖，在奥地利有卓越的影响力。Heitzinger的近160篇论文发表在国际高水平学术期刊与会议上，另有教材著作“Algorithms in Julia”将于2019年7月份由著名学术出版商Springer出版。研究领域：Reinforcement learning强化学习；Bayesian estimation and Bayesian inversion贝叶斯估计与贝叶斯反演；(Stochastic) partial differential equations with applications in nanotechnology, sensors, and metamaterials（随机）偏微分方程式在纳米技术、传感器和超材料上的应用。