{"title":"Application of Physics Informed Neural Net-work Method Based on Numerical Differentiation in Non-Rectangular Regions","authors":"豪 康","doi":"10.12677/ijfd.2023.112007","DOIUrl":null,"url":null,"abstract":"Physics-Informed Neural Networks (PINN) is a novel data-driven numerical framework for solving","PeriodicalId":66025,"journal":{"name":"流体动力学","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"流体动力学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.12677/ijfd.2023.112007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Physics-Informed Neural Networks (PINN) is a novel data-driven numerical framework for solving
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