Quantifying sampling noise and parametric uncertainty in atomistic-to-continuum simulations using surrogate models
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:
-
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Publication Date:
- 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.
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
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.
@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}
}
Web of Science