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Accelerating multiscale modelling of fluids with on-the-fly Gaussian process regression
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Stephenson, David, Kermode, James R. and Lockerby, Duncan A. (2018) Accelerating multiscale modelling of fluids with on-the-fly Gaussian process regression. Microfluidics and Nanofluidics, 22 . 139. doi:10.1007/s10404-018-2164-z ISSN 1613-4982.
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Official URL: https://doi.org/10.1007/s10404-018-2164-z
Abstract
We present a scheme for accelerating hybrid continuum-atomistic models in multiscale fluidic systems by using Gaussian process regression as a surrogate model for computationally expensive molecular dynamics simulations. Using Gaussian process regression, we are able to accurately predict atomic-scale information purely by consideration of the macroscopic continuum-model inputs and outputs and judge on the fly whether the uncertainty of our prediction is at an acceptable level, else a new molecular simulation is performed to continually augment the database, which is never required to be complete. This provides a substantial improvement over the current generation of hybrid methods, which often require many similar atomistic simulations to be performed, discarding information after it is used once. We apply our hybrid scheme to nano-confined unsteady flow through a high-aspect-ratio converging–diverging channel, and make comparisons between the new scheme and full molecular dynamics simulations for a range of uncertainty thresholds and initial databases. For low thresholds, our hybrid solution is highly accurate—around that of thermal noise. As the uncertainty threshold is raised, the accuracy of our scheme decreases and the computational speed-up increases (relative to a full molecular simulation), enabling the compromise between accuracy and efficiency to be tuned. The speed-up of our hybrid solution ranges from an order of magnitude, with no initial database, to cases where an extensive initial database ensures no new MD simulations are required.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QP Physiology T Technology > TJ Mechanical engineering and machinery |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Molecular dynamics -- Simulation methods, Microfluidics, Machine learning, Gaussian processes | |||||||||||||||
Journal or Publication Title: | Microfluidics and Nanofluidics | |||||||||||||||
Publisher: | Springer | |||||||||||||||
ISSN: | 1613-4982 | |||||||||||||||
Official Date: | December 2018 | |||||||||||||||
Dates: |
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Volume: | 22 | |||||||||||||||
Article Number: | 139 | |||||||||||||||
DOI: | 10.1007/s10404-018-2164-z | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 26 November 2018 | |||||||||||||||
Date of first compliant Open Access: | 27 November 2018 | |||||||||||||||
RIOXX Funder/Project Grant: |
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