{"title":"小型工作坊:数据驱动的最优控制分析","authors":"Lars Grüne, K. Morris","doi":"10.4171/owr/2021/23","DOIUrl":null,"url":null,"abstract":". This hybrid mini-workshop discussed recent mathematical methods for analyzing the opportunities and limitations of data-driven and ma-chine-learning approaches to optimal feedback control. The analysis con-cerned all aspects of such approaches, ranging from approximation theory particularly for high-dimensional problems via complexity analysis of algorithms to robustness issues.","PeriodicalId":436142,"journal":{"name":"Oberwolfach Reports","volume":"188 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mini-Workshop: Analysis of Data-driven Optimal Control\",\"authors\":\"Lars Grüne, K. Morris\",\"doi\":\"10.4171/owr/2021/23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". This hybrid mini-workshop discussed recent mathematical methods for analyzing the opportunities and limitations of data-driven and ma-chine-learning approaches to optimal feedback control. The analysis con-cerned all aspects of such approaches, ranging from approximation theory particularly for high-dimensional problems via complexity analysis of algorithms to robustness issues.\",\"PeriodicalId\":436142,\"journal\":{\"name\":\"Oberwolfach Reports\",\"volume\":\"188 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oberwolfach Reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4171/owr/2021/23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oberwolfach Reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4171/owr/2021/23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mini-Workshop: Analysis of Data-driven Optimal Control
. This hybrid mini-workshop discussed recent mathematical methods for analyzing the opportunities and limitations of data-driven and ma-chine-learning approaches to optimal feedback control. The analysis con-cerned all aspects of such approaches, ranging from approximation theory particularly for high-dimensional problems via complexity analysis of algorithms to robustness issues.
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