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An artificial neural network-based multiscale method for hybrid atomistic-continuum simulations

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Abstract

This paper presents an artificial neural network-based multiscale method for coupling continuum and molecular simulations. Molecular dynamics modelling is employed as a local “high resolution” refinement of computational data required by the continuum computational fluid dynamics solver. The coupling between atomistic and continuum simulations is obtained by an artificial neural network (ANN) methodology. The ANN aims to optimise the transfer of information through minimisation of (1) the computational cost by avoiding repetitive atomistic simulations of nearly identical states, and (2) the fluctuation strength of the atomistic outputs that are fed back to the continuum solver. Results are presented for prototype flows such as the isothermal Couette flow with slip boundary conditions and the slip Couette flow with heat transfer.

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  1. Department of Engineering Physics, Cranfield University, Cranfield, UK

    Nikolaos Asproulis & Dimitris Drikakis

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  1. Nikolaos Asproulis
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  2. Dimitris Drikakis
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Correspondence to Nikolaos Asproulis.

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Asproulis, N., Drikakis, D. An artificial neural network-based multiscale method for hybrid atomistic-continuum simulations. Microfluid Nanofluid 15, 559–574 (2013). https://doi.org/10.1007/s10404-013-1154-4

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