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Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis

Publication ,  Journal Article
Wang, H; Guilleminot, J; Soize, C
Published in: Computer Methods in Applied Mechanics and Engineering
September 1, 2019

A methodology enabling the robust treatment of model-form uncertainties in molecular dynamics simulations is proposed. The approach consists in properly randomizing a reduced-order basis, obtained by the method of snapshots in the configuration space. A multi-step strategy to identify the hyperparameters in the stochastic reduced-order basis is further introduced. The relevance of the framework is finally demonstrated by characterizing various types of modeling errors associated with molecular dynamics simulations on a graphene sheet. In particular, the ability of the framework to represent uncertainties raised by model reduction and interatomic potential selection is assessed.

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Published In

Computer Methods in Applied Mechanics and Engineering

DOI

ISSN

0045-7825

Publication Date

September 1, 2019

Volume

354

Start / End Page

37 / 55

Related Subject Headings

  • Applied Mathematics
  • 49 Mathematical sciences
  • 40 Engineering
  • 09 Engineering
  • 01 Mathematical Sciences
 

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Wang, H., Guilleminot, J., & Soize, C. (2019). Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis. Computer Methods in Applied Mechanics and Engineering, 354, 37–55. https://doi.org/10.1016/j.cma.2019.05.020
Wang, H., J. Guilleminot, and C. Soize. “Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis.” Computer Methods in Applied Mechanics and Engineering 354 (September 1, 2019): 37–55. https://doi.org/10.1016/j.cma.2019.05.020.
Wang H, Guilleminot J, Soize C. Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis. Computer Methods in Applied Mechanics and Engineering. 2019 Sep 1;354:37–55.
Wang, H., et al. “Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis.” Computer Methods in Applied Mechanics and Engineering, vol. 354, Sept. 2019, pp. 37–55. Scopus, doi:10.1016/j.cma.2019.05.020.
Wang H, Guilleminot J, Soize C. Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis. Computer Methods in Applied Mechanics and Engineering. 2019 Sep 1;354:37–55.
Journal cover image

Published In

Computer Methods in Applied Mechanics and Engineering

DOI

ISSN

0045-7825

Publication Date

September 1, 2019

Volume

354

Start / End Page

37 / 55

Related Subject Headings

  • Applied Mathematics
  • 49 Mathematical sciences
  • 40 Engineering
  • 09 Engineering
  • 01 Mathematical Sciences