Bio: Khachik Sargsyan is a Principal Member of Technical Staff at Sandia National Laboratories (SNL) in Livermore, CA. Before staff and postdoctoral positions at SNL, he received his Ph.D. in Applied and Interdisciplinary Mathematics from University of Michigan, Ann Arbor, in 2007. His Bachelors degree, awarded in 2002, is in Applied Math and Physics from Moscow Institute of Physics and Technology. Dr. Sargsyan’s research evolves around uncertainty quantification (UQ) and predictability analysis of physical and computational models. He has developed and applied methods for model reduction, UQ and data assimilation, targeting fundamental challenges such as structural errors, intrinsic stochasticity, high-dimensionality, limited data, discontinuities and rare events, with applications in climate modeling, chemical kinetics, hardware architecture simulators and turbulent combustion. He is one of the lead developers of UQTk (www.sandia.gov/uqtoolkit), a lightweight C++/Python software toolkit for quantification of uncertainties in model predictions.

Dr. Sargsyan is being hosted by Prof. Ivanov (Civil and Env. Engineering). If you would like to meet him, please send an email to Chase Dwelle at [email protected]

Probabilistic Methods for Uncertainty Quantification in Computational Models

Over the last decade, improved measurement capabilities and computational resources have led to significant algorithmic developments toward efficient uncertainty quantification (UQ) for computational models. Such models of physical systems often involve input parameters that exhibit certain degree of uncertainty. Estimation and propagation of these uncertainties are crucial for model validation, computational/experimental design and decision making. ​This talk will focus on probabilistic methods with emphasis on Polynomial Chaos (PC) expansions as a means for functional representation of random variables. The talk will highlight the use of PC methods both for forward propagation of uncertainties and for inverse problems, such as parameter estimation via Bayesian inference. I will list associated major challenges, including the curse of dimensionality and model structural error estimation, in the context of computationally expensive models of physical systems. Both fundamental and more recent methods will be introduced and demonstrated, impacting a wide range of applications, such as climate modeling, turbulent combustion and chemical kinetics.