Matches in SemOpenAlex for { <https://semopenalex.org/work/W3037136087> ?p ?o ?g. }
- W3037136087 endingPage "25" @default.
- W3037136087 startingPage "11" @default.
- W3037136087 abstract "The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable optimal control of a stochastic multiscale system subject to parametric uncertainty. The system used for the case study was a simulation of thin film formation by chemical vapour deposition, where a solid-on-solid kinetic Monte Carlo model was coupled with continuum transport equations. The ANNs were trained to estimate the dynamic responses of statistical moments of the system’s observables and subsequently employed in a dynamic optimization scheme to identify the optimal profiles of the manipulated variables that would attain the desired thin film properties at the end of the batch. The resulting profiles were validated using the stochastic multiscale system and a close agreement with ANN-based predictions was observed. Due to their computational efficiency, accuracy, and the ability to reject disturbances, the ANNs appear to be an attractive approach for the optimization and control of computationally demanding multiscale process systems." @default.
- W3037136087 created "2020-07-02" @default.
- W3037136087 creator A5043725286 @default.
- W3037136087 creator A5063715865 @default.
- W3037136087 date "2020-09-01" @default.
- W3037136087 modified "2023-10-04" @default.
- W3037136087 title "Artificial Neural Networks for dynamic optimization of stochastic multiscale systems subject to uncertainty" @default.
- W3037136087 cites W1197831148 @default.
- W3037136087 cites W1510053011 @default.
- W3037136087 cites W1523740859 @default.
- W3037136087 cites W1691093109 @default.
- W3037136087 cites W187078150 @default.
- W3037136087 cites W1965618643 @default.
- W3037136087 cites W1966097220 @default.
- W3037136087 cites W1968263491 @default.
- W3037136087 cites W1970338077 @default.
- W3037136087 cites W1972291235 @default.
- W3037136087 cites W1975327134 @default.
- W3037136087 cites W1975545063 @default.
- W3037136087 cites W1988115241 @default.
- W3037136087 cites W1995341919 @default.
- W3037136087 cites W2017222247 @default.
- W3037136087 cites W2024442222 @default.
- W3037136087 cites W2028501442 @default.
- W3037136087 cites W2032676284 @default.
- W3037136087 cites W2035439205 @default.
- W3037136087 cites W2042021082 @default.
- W3037136087 cites W2042535339 @default.
- W3037136087 cites W2048060899 @default.
- W3037136087 cites W2049111698 @default.
- W3037136087 cites W2050738787 @default.
- W3037136087 cites W2052412727 @default.
- W3037136087 cites W2056880304 @default.
- W3037136087 cites W2057896693 @default.
- W3037136087 cites W2061502779 @default.
- W3037136087 cites W2079000916 @default.
- W3037136087 cites W2079744160 @default.
- W3037136087 cites W2089634260 @default.
- W3037136087 cites W2089719636 @default.
- W3037136087 cites W2092757808 @default.
- W3037136087 cites W2107084461 @default.
- W3037136087 cites W2113337191 @default.
- W3037136087 cites W2119592775 @default.
- W3037136087 cites W2124153771 @default.
- W3037136087 cites W2127951107 @default.
- W3037136087 cites W2147912439 @default.
- W3037136087 cites W2164343005 @default.
- W3037136087 cites W2169423829 @default.
- W3037136087 cites W2171594132 @default.
- W3037136087 cites W2195327783 @default.
- W3037136087 cites W2238431993 @default.
- W3037136087 cites W2291099030 @default.
- W3037136087 cites W2463622642 @default.
- W3037136087 cites W2507306503 @default.
- W3037136087 cites W2514563055 @default.
- W3037136087 cites W2593467507 @default.
- W3037136087 cites W2595216362 @default.
- W3037136087 cites W2621318220 @default.
- W3037136087 cites W2623263577 @default.
- W3037136087 cites W2793228372 @default.
- W3037136087 cites W2809250211 @default.
- W3037136087 cites W2883194111 @default.
- W3037136087 cites W2885404289 @default.
- W3037136087 cites W2887661471 @default.
- W3037136087 cites W2890431580 @default.
- W3037136087 cites W2896937211 @default.
- W3037136087 cites W2903010511 @default.
- W3037136087 cites W2914605496 @default.
- W3037136087 cites W2918623965 @default.
- W3037136087 cites W2945439698 @default.
- W3037136087 cites W2962759047 @default.
- W3037136087 cites W2964182357 @default.
- W3037136087 cites W2966398373 @default.
- W3037136087 cites W3002159590 @default.
- W3037136087 cites W3011127702 @default.
- W3037136087 cites W3099674394 @default.
- W3037136087 cites W3099803807 @default.
- W3037136087 cites W3121339476 @default.
- W3037136087 doi "https://doi.org/10.1016/j.cherd.2020.06.017" @default.
- W3037136087 hasPublicationYear "2020" @default.
- W3037136087 type Work @default.
- W3037136087 sameAs 3037136087 @default.
- W3037136087 citedByCount "10" @default.
- W3037136087 countsByYear W30371360872020 @default.
- W3037136087 countsByYear W30371360872021 @default.
- W3037136087 countsByYear W30371360872022 @default.
- W3037136087 countsByYear W30371360872023 @default.
- W3037136087 crossrefType "journal-article" @default.
- W3037136087 hasAuthorship W3037136087A5043725286 @default.
- W3037136087 hasAuthorship W3037136087A5063715865 @default.
- W3037136087 hasConcept C105795698 @default.
- W3037136087 hasConcept C111919701 @default.
- W3037136087 hasConcept C117251300 @default.
- W3037136087 hasConcept C126255220 @default.
- W3037136087 hasConcept C147168706 @default.
- W3037136087 hasConcept C154945302 @default.
- W3037136087 hasConcept C194387892 @default.
- W3037136087 hasConcept C19499675 @default.