K
Khachik Sargsyan
Researcher at Sandia National Laboratories
Publications - 106
Citations - 2053
Khachik Sargsyan is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Uncertainty quantification & Polynomial chaos. The author has an hindex of 20, co-authored 94 publications receiving 1647 citations. Previous affiliations of Khachik Sargsyan include University of Michigan & Oak Ridge National Laboratory.
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Extinction Times for Birth-Death Processes: Exact Results, Continuum Asymptotics, and the Failure of the Fokker--Planck Approximation
TL;DR: In this article, extinction times for a class of birth-death processes commonly found in applications were considered, where there is a control parameter which defines a threshold, below which the population quickly becomes extinct; above, it persists for a long time.
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Enhancing ℓ 1 -minimization estimates of polynomial chaos expansions using basis selection
TL;DR: It is shown that for a given computational budget, basis selection produces a more accurate PCE than would be obtained if the basis were fixed a priori.
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Extinction times for birth-death processes: exact results, continuum asymptotics, and the failure of the Fokker-Planck approximation
TL;DR: An exact expression is given for the mean time to extinction in the discrete case and its asymptotic expansion for large values of the population scale and it is observed that the Fokker--Planck approximation is valid only quite near the threshold.
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Dimensionality reduction for complex models via bayesian compressive sensing
Khachik Sargsyan,Cosmin Safta,Habib N. Najm,Bert Debusschere,Daniel M. Ricciuto,Peter E. Thornton +5 more
TL;DR: This work implements a PC-based surrogate model construction that “learns” and retains only the most relevant basis terms of the PC expansion, using sparse Bayesian learning, which dramatically reduces the dimensionality of the problem, making it more amenable to further analysis such as sensitivity or calibration studies.
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On the Statistical Calibration of Physical Models
TL;DR: A novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model, is introduced, showing that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean-square sense with the discrepancy from the detailed model data.