{"title":"控制的代理模型和混合模型","authors":"B. Lie","doi":"10.3384/ecp201701","DOIUrl":null,"url":null,"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.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Surrogate and Hybrid Models for Control\",\"authors\":\"B. Lie\",\"doi\":\"10.3384/ecp201701\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":179867,\"journal\":{\"name\":\"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3384/ecp201701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3384/ecp201701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.