{"title":"与作者见面蒋子欣和董冰","authors":"","doi":"10.1016/j.patter.2024.101044","DOIUrl":null,"url":null,"abstract":"<p>What can we do to mitigate climate change and achieve carbon neutrality for buildings? In their recent publication in <em>Patterns</em>, the authors proposed a modularized neural network incorporating physical priors for future building energy modeling, paving the way for scalable and reliable building energy modeling, optimization, retrofit designs, and buildings-to-grid integration. In this interview, the authors talk about incorporating fundamental heat transfer and thermodynamics knowledge into data-driven models.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"65 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Meet the authors: Zixin Jiang and Bing Dong\",\"authors\":\"\",\"doi\":\"10.1016/j.patter.2024.101044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>What can we do to mitigate climate change and achieve carbon neutrality for buildings? In their recent publication in <em>Patterns</em>, the authors proposed a modularized neural network incorporating physical priors for future building energy modeling, paving the way for scalable and reliable building energy modeling, optimization, retrofit designs, and buildings-to-grid integration. In this interview, the authors talk about incorporating fundamental heat transfer and thermodynamics knowledge into data-driven models.</p>\",\"PeriodicalId\":36242,\"journal\":{\"name\":\"Patterns\",\"volume\":\"65 1\",\"pages\":\"\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Patterns\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.patter.2024.101044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patterns","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.patter.2024.101044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
What can we do to mitigate climate change and achieve carbon neutrality for buildings? In their recent publication in Patterns, the authors proposed a modularized neural network incorporating physical priors for future building energy modeling, paving the way for scalable and reliable building energy modeling, optimization, retrofit designs, and buildings-to-grid integration. In this interview, the authors talk about incorporating fundamental heat transfer and thermodynamics knowledge into data-driven models.