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

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.
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  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1540-3467; 539954
Grant/Contract Number:
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
Research Org:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY sampling noise; atomistic; continuum; parametric uncertainty; polynomial chaos; multiscale