Woodruff School of Mechanical Engineering

Guest Speaker

Title:

Data-Driven Uncertainty Quantification Algorithm and Its Application in Complex Biological and Environmental Systems

Speaker:

Dr. Guang Lin

Affiliation:

Pacific Northwest National Laboratory

When:

Friday, April 4, 2014 at 10:30:00 AM

Where:

Boggs Building, Room 3-47

Host:

Yan Wang
yan.wang@me.gatech.edu
1-404-894-4714

Abstract

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 very large-scale complex systems. Although remarkable progresses have been made, UQ simulations remain challenging due to their exceedingly high simulation cost for large-scale complex systems. In this talk, a new formulation for analyzing uncertainty sensitivity, quantifying uncertainty and visualizing uncertainty will be discussed. An integrated simulation framework will be presented that quantifies both numerical and modeling errors in an effort to establish error bars in numerical simulations. In particular, stochastic formulations based on Galerkin and collocation versions of the generalized Polynomial Chaos (gPC) will be discussed. Additionally, we will present some effective new ways of dealing with this curse of dimensionality. Particularly, adaptive ANOVA decomposition, and some stochastic sensitivity analysis techniques will be discussed in some detail. Several specific examples on sensitivity analysis and predictive modeling of thrombin production in blood coagulation chemical reaction network, flow and transport in randomly heterogeneous porous media, and uncertainty quantification in climate modeling will be presented to illustrate the main idea of our approach.


Biography

Guang Lin received his Ph.D. from Division of Applied Mathematics at Brown University in 2007. Currently, he is a senior staff scientist in Computational Mathematics Group at Pacific Northwest National Laboratory (PNNL). He will move to Purdue University this August as an Assistant Professor of the Department of Mechanical Engineering and Department of Mathematics. He received 2012 DOE Ronald L. Brodzinski Award for Early Career Exception Achievement, 2010 DOE ASCR Leadership Computing Challenge award, 2010 Outstanding Performance Award at PNNL, and Ostrach Fellowship at Brown University. He serves on the editorial board of International Journal of Uncertainty Quantification, Journal of Stochastics, and Scientific World Journal. His research focuses on uncertainty quantification and multiscale modeling of complex systems.

Notes

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