W. Xiong, Pan Liu, Zhangchun Tang, Yan Shi, Chencheng Liu, Fanyu Qu, Gaoyang Liu, Qiang Gao
{"title":"基于生成对抗网络的融合物理设计","authors":"W. Xiong, Pan Liu, Zhangchun Tang, Yan Shi, Chencheng Liu, Fanyu Qu, Gaoyang Liu, Qiang Gao","doi":"10.1145/3579654.3579760","DOIUrl":null,"url":null,"abstract":"The physical design of fusion is a key part of achieving controlled thermonuclear fusion. The physical design of fusion is divided into three important parts. These are the structural design, the material properties and the physical processes. These three components form a structural design file, a material properties file and a physical process file, which are fed into a configuration operation to generate an adversarial network to determine the final process parameters. The process parameters can be used for the physical design and fabrication of the fusion target as well as for the overall fusion experiments. The fusion physics model obtained by GAN has an output energy of up to 300 MJ, a gain of up to 30 and a neutron yield of 1017-1019, which meets the conditions for fusion ignition.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Physical design of fusion based on generative adversarial networks\",\"authors\":\"W. Xiong, Pan Liu, Zhangchun Tang, Yan Shi, Chencheng Liu, Fanyu Qu, Gaoyang Liu, Qiang Gao\",\"doi\":\"10.1145/3579654.3579760\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The physical design of fusion is a key part of achieving controlled thermonuclear fusion. The physical design of fusion is divided into three important parts. These are the structural design, the material properties and the physical processes. These three components form a structural design file, a material properties file and a physical process file, which are fed into a configuration operation to generate an adversarial network to determine the final process parameters. The process parameters can be used for the physical design and fabrication of the fusion target as well as for the overall fusion experiments. The fusion physics model obtained by GAN has an output energy of up to 300 MJ, a gain of up to 30 and a neutron yield of 1017-1019, which meets the conditions for fusion ignition.\",\"PeriodicalId\":146783,\"journal\":{\"name\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3579654.3579760\",\"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 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579654.3579760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Physical design of fusion based on generative adversarial networks
The physical design of fusion is a key part of achieving controlled thermonuclear fusion. The physical design of fusion is divided into three important parts. These are the structural design, the material properties and the physical processes. These three components form a structural design file, a material properties file and a physical process file, which are fed into a configuration operation to generate an adversarial network to determine the final process parameters. The process parameters can be used for the physical design and fabrication of the fusion target as well as for the overall fusion experiments. The fusion physics model obtained by GAN has an output energy of up to 300 MJ, a gain of up to 30 and a neutron yield of 1017-1019, which meets the conditions for fusion ignition.