Quantifying sampling noise and parametric uncertainty in atomistic-to-continuum simulations using surrogate models

Salloum, Maher N.; Sargsyan, Khachik; Jones, Reese E.; Najm, Habib N.; Debusschere, Bert

doi:10.1137/140989601

Title: Quantifying sampling noise and parametric uncertainty in atomistic-to-continuum simulations using surrogate models

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Abstract

We present a methodology to assess the predictive fidelity of multiscale simulations by incorporating uncertainty in the information exchanged between the components of an atomistic-to-continuum simulation. We account for both the uncertainty due to finite sampling in molecular dynamics (MD) simulations and the uncertainty in the physical parameters of the model. Using Bayesian inference, we represent the expensive atomistic component by a surrogate model that relates the long-term output of the atomistic simulation to its uncertain inputs. We then present algorithms to solve for the variables exchanged across the atomistic-continuum interface in terms of polynomial chaos expansions (PCEs). We also consider a simple Couette flow where velocities are exchanged between the atomistic and continuum components, while accounting for uncertainty in the atomistic model parameters and the continuum boundary conditions. Results show convergence of the coupling algorithm at a reasonable number of iterations. As a result, the uncertainty in the obtained variables significantly depends on the amount of data sampled from the MD simulations and on the width of the time averaging window used in the MD simulations.

Authors:

Salloum, Maher N. ^[1]; Sargsyan, Khachik ^[1]; Jones, Reese E. ^[1]; Najm, Habib N. ^[1]; Debusschere, Bert ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Publication Date:: Tue Aug 11 00:00:00 EDT 2015

Research Org.:: Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1184470

Alternate Identifier(s):: OSTI ID: 1237659

Report Number(s):: SAND-2014-18430J; SAND-2015-4693J
Journal ID: ISSN 1540-3467; 539954

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: Multiscale Modeling & Simulation (Online)

Additional Journal Information:: Journal Name: Multiscale Modeling & Simulation (Online); Journal Volume: 13; Journal Issue: 3; Journal ID: ISSN 1540-3467

Publisher:: SIAM

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; sampling noise; atomistic; continuum; parametric uncertainty; polynomial chaos; multiscale; 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Salloum, Maher N., Sargsyan, Khachik, Jones, Reese E., Najm, Habib N., and Debusschere, Bert. Quantifying sampling noise and parametric uncertainty in atomistic-to-continuum simulations using surrogate models.  United States: N. p., 2015. 
Web.  doi:10.1137/140989601.

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                    Salloum, Maher N., Sargsyan, Khachik, Jones, Reese E., Najm, Habib N., & Debusschere, Bert. Quantifying sampling noise and parametric uncertainty in atomistic-to-continuum simulations using surrogate models.  United States.  https://doi.org/10.1137/140989601

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                    Salloum, Maher N., Sargsyan, Khachik, Jones, Reese E., Najm, Habib N., and Debusschere, Bert. Tue .  
"Quantifying sampling noise and parametric uncertainty in atomistic-to-continuum simulations using surrogate models".  United States.  https://doi.org/10.1137/140989601.  https://www.osti.gov/servlets/purl/1184470.

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@article{osti_1184470,

  title        = {Quantifying sampling noise and parametric uncertainty in atomistic-to-continuum simulations using surrogate models},

  author       = {Salloum, Maher N. and Sargsyan, Khachik and Jones, Reese E. and Najm, Habib N. and Debusschere, Bert},

  abstractNote = {We present a methodology to assess the predictive fidelity of multiscale simulations by incorporating uncertainty in the information exchanged between the components of an atomistic-to-continuum simulation. We account for both the uncertainty due to finite sampling in molecular dynamics (MD) simulations and the uncertainty in the physical parameters of the model. Using Bayesian inference, we represent the expensive atomistic component by a surrogate model that relates the long-term output of the atomistic simulation to its uncertain inputs. We then present algorithms to solve for the variables exchanged across the atomistic-continuum interface in terms of polynomial chaos expansions (PCEs). We also consider a simple Couette flow where velocities are exchanged between the atomistic and continuum components, while accounting for uncertainty in the atomistic model parameters and the continuum boundary conditions. Results show convergence of the coupling algorithm at a reasonable number of iterations. As a result, the uncertainty in the obtained variables significantly depends on the amount of data sampled from the MD simulations and on the width of the time averaging window used in the MD simulations.},

  doi          = {10.1137/140989601},

  journal      = {Multiscale Modeling & Simulation (Online)},

  number       = 3,

  volume       = 13,

  place        = {United States},

  year         = {Tue Aug 11 00:00:00 EDT 2015},

  month        = {Tue Aug 11 00:00:00 EDT 2015}

}

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