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Sensitivity analysis, Uncertainty quantification and Parameter Estimation of Complex Biological and Environmental Systems
Date:2016/6/26     Browse:338

Sensitivity analysis, Uncertainty quantification and Parameter Estimation of Complex Biological and Environmental Systems

Speaker: Guang Lin

Time: Jun 26, 10:00am - 11:00am.

Location: Room 109, H2 Center

Abstract:

There are many uncertainties in modeling of complex biological and environmental systems. Experience suggests that uncertainties often play an important role in quantifying the performance of complex systems. Therefore, uncertainty needs to be treated as a core element in modeling, simulation and optimization of complex systems. The field of uncertainty quantification (UQ) has received an increasing amount of attention recently. Extensive research efforts have been devoted to it and many novel numerical techniques have been developed. These techniques aim to conduct stochastic simulations for data-driven large-scale complex systems.

 

In this talk, we will present some effective new ways of dealing with the “curse of dimensionality” and “parameter estimation” challenges. Particularly, adaptive compressive sensing-based uncertainty quantification algorithms will be discussed in some detail. First, the active subspace algorithm will be introduced to identify the intrinsically low dimensional subspace to greatly improve the accuracy and efficiency of compressive sensing algorithm in the high-dimensional UQ problem. We will illustrate the main idea of our developed high-dimensional UQ algorithm using a groundwater flow in an aquifer problem.

Bio:

林光博士,美国布朗大学应用数学博士,主要从事复杂流体问题的多尺度模拟,不确定性,和大数据分析软件的开发,在高精度数值格式,和生物流体模拟等领域都有丰富的工作经历。先后供职于美国能源部西北太平洋实验室,和美国普度大学。现于美国普度大学数学系和机械工程系任助理教授。曾获得2016年美国自然基金会早期职业生涯杰出成就奖,2015年美国数学生物科学研究所早期职业生涯杰出成就奖,以及2012年美国能源部西北太平洋实验室 Ronald L. Brodzinski早期职业生涯杰出成就奖。

 

SIST-Seminar 16051