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

Abstract : 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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Submitted on : Monday, June 3, 2019 - 6:40:34 PM
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Haoran Wang, Johann Guilleminot, Christian Soize. Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis. Computer Methods in Applied Mechanics and Engineering, Elsevier, 2019, 354, pp.37-55. ⟨10.1016/j.cma.2019.05.020⟩. ⟨hal-02146341⟩

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