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dc.contributor.authorChaffart, Donovan
dc.contributor.authorRicardez-Sandoval, Luis A. 17:13:52 (GMT) 17:13:52 (GMT)
dc.descriptionThe final publication is available at Elsevier via © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.description.abstractThis work details the construction and evaluation of a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model. The multiscale model combines continuum differential equations, which describe the transport of the precursor gas phase, with a stochastic partial differential equation (SPDE) that predicts the evolution of the thin film surface. In order to allow the SPDE to accurately predict the thin film growth over a range of system parameters, an ANN is developed and trained to predict the values of the SPDE coefficients. The fully-assembled hybrid multiscale model is validated through comparison against a kinetic Monte Carlo-based thin film multiscale model. The model is subsequently applied to a series of optimization and control studies to test its performance under different scenarios. These studies illustrate the computational efficiency of the proposed hybrid multiscale model for optimization and control applications.en
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canadaen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectArtificial neural networksen
dc.subjectHybrid modellingen
dc.subjectMultiscale modellingen
dc.subjectStochastic partial differential equationsen
dc.subjectThin film depositionen
dc.titleOptimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approachen
dcterms.bibliographicCitationChaffart, D., & Ricardez-Sandoval, L. A. (2018). Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach. Computers & Chemical Engineering, 119, 465–479. doi:10.1016/j.compchemeng.2018.08.029en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Chemical Engineeringen

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