{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T21:30:54Z","timestamp":1769549454555,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683003","type":"print"},{"value":"9781643683010","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,10]]},"abstract":"<jats:p>When analyzing the thermal stress and deformation of satellites in orbit, the traditional numerical methods, such as the finite difference and the finite element, are expensive and time-consuming. To improve computational efficiency, we propose a deep-learning based surrogate to immediately predict the thermal stress and deformation of a satellite with a given temperature field, where the U-Net is employed to learn the end-to-end mapping from the temperature field to the thermal stress and deformation. A data set with less smooth temperature fields is generated to augment the training data, by which the accuracy and generalization performance of the model is significantly improved. Combined with a rapid temperature prediction method, the model predicts the thermal stress and deformation of a satellite motherboard given several heat sources, verifying the feasibility and effectiveness of the proposed method.<\/jats:p>","DOI":"10.3233\/faia220128","type":"book-chapter","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T23:22:12Z","timestamp":1660519332000},"source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Based Thermal Stress and Deformation Analysis of Satellites"],"prefix":"10.3233","author":[{"given":"Zeyu","family":"Cao","sequence":"first","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China"},{"name":"The No.92941st Troop of PLA, Huludao 125001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoya","family":"Zhang","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Yao","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Modern Management based on Big Data III"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220128","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T23:22:14Z","timestamp":1660519334000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,10]]},"ISBN":["9781643683003","9781643683010"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220128","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,10]]}}}