{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T01:58:39Z","timestamp":1769565519959,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"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":[[2026,1,27]]},"abstract":"<jats:p>This report proposes the development and implementation of an innovative machine learning surrogate meshfree approach for modelling heat transfer phenomena in industrial processes with moving heat sources. This approach is based on the Finite Pointset Method (FPM), a Generalized Finite Differences Method (GFDM) which is enhanced in this work with machine learning techniques for the construction of shape functions. The objective of this development is to overcome the limitations posed by commonly used mesh-based methods and to improve the efficiency found in standard mesh-free methods. The tool resulting from this work contributes to the positive impact of mathematical modelling developments with mesh-free methods and expands their applications in areas of science and industry. The accuracy, feasibility, and robustness of this new formulation are evaluated in detail through a series of case studies, starting with simple cases, for which analytical solutions exist, and evolving to complex and practical examples, representative of selected applications involving moving heat sources. The results of these examples demonstrate that this meshless numerical tool is promising, and furthermore, similar strategies could be proposed for modelling other physical phenomena or their coupling.<\/jats:p>","DOI":"10.3233\/faia251657","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:19:03Z","timestamp":1769519943000},"source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning Surrogate Meshfree Approach for Thermal Analysis with Moving Heat Sources"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8434-6129","authenticated-orcid":false,"given":"Felix R.","family":"Saucedo-Zendejo","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Matem\u00e1ticas Aplicadas, Universidad Aut\u00f3noma de Coahuila, M\u00e9xico"}]},{"given":"Natalia","family":"Galvan-Camara","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Matem\u00e1ticas Aplicadas, Universidad Aut\u00f3noma de Coahuila, M\u00e9xico"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining XI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251657","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:19:03Z","timestamp":1769519943000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"ISBN":["9781643686448"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251657","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}