dc.contributor.author | Lie, Bernt | |
dc.date.accessioned | 2020-03-05T09:45:18Z | |
dc.date.available | 2020-03-05T09:45:18Z | |
dc.date.created | 2020-01-21T11:20:53Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Linköping Electronic Conference Proceedings. 2019, (170), 1-8. | en_US |
dc.identifier.issn | 1650-3686 | |
dc.identifier.uri | https://hdl.handle.net/11250/2645422 | |
dc.description.abstract | With access to fast computers and efficient machine learning tools, it is of interest to use machine learning to develop surrogate models from complex physics-based models. Next, a hybrid model is a combination model where a data driven model is built to describe the difference between an imperfect physics-based/surrogate model and experimental data. Availability of Big Data makes it possible to gradually improve on a hybrid model as more data become available. In this paper, an overview is given of relevant ideas from model approximation/data driven models for dynamic systems, and machine learning via artificial neural networks. To illustrate how the ideas can be implemented in practice, a simple introduction to package Flux for language Julia is given. Several types of surrogate models are developed for a simple, illustrative system. Finally, the development of a hybrid model is illustrated. Emphasis is put on ideas related to Digital Twins for control. | en_US |
dc.language.iso | eng | en_US |
dc.relation.uri | http://www.ep.liu.se/ecp/170/001/ecp19170001.pdf | |
dc.rights | Navngivelse-Ikkekommersiell 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/deed.no | * |
dc.title | Surrogate and Hybrid Models for Control | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 1-8 | en_US |
dc.source.journal | Linköping Electronic Conference Proceedings | en_US |
dc.source.issue | 170 | en_US |
dc.identifier.doi | 10.3384/ecp201701 | |
dc.identifier.cristin | 1779048 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |