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.
Papers
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Journal ArticleDOI
Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting
Valeriy Y. Ivanov,Donghui Xu,M. Chase Dwelle,Khachik Sargsyan,Daniel B. Wright,Nikolaos D. Katopodes,Jongho Kim,Vinh Ngoc Tran,A. M. Warnock,Simone Fatichi,Paolo Burlando,Enrica Caporali,Pedro Restrepo,Brett F. Sanders,Molly M. Chaney,Ana M. B. Nunes,Fernando Nardi,Enrique R. Vivoni,Erkan Istanbulluoglu,Gautam Bisht,Rafael L. Bras +20 more
TL;DR: This work presents a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods and has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real-time.
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Uncertainty quantification in LES of channel flow
Cosmin Safta,Myra Blaylock,Jeremy Alan Templeton,Stefan P. Domino,Khachik Sargsyan,Habib N. Najm +5 more
TL;DR: In this article, a Bayesian framework for estimating joint densities for large-Eddy Simulation (LES) sub-grid scale model parameters based on canonical forced isotropic turbulence Direct Numerical Simulation (DNS) data is presented.
Journal ArticleDOI
Data-free inference of uncertain parameters in chemical models
TL;DR: In this paper, a data-free inference procedure for estimating uncertain model parameters for a chemical model of methane-air ignition is presented, which involves a nested pair of Markov chains, exploring both the data and parametric spaces, to discover a pooled joint posterior consistent with available information.
Journal ArticleDOI
A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations
Maher Salloum,Khachik Sargsyan,Reese E. Jones,Bert Debusschere,Habib N. Najm,Helgi Adalsteinsson +5 more
TL;DR: An iterative stochastic coupling algorithm that relies on Bayesian inference to build polynomial chaos expansions for the variables exchanged across the atomistic-continuum interface to assess the predictive fidelity of multiscale simulations.