{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:07:11Z","timestamp":1774922831315,"version":"3.50.1"},"reference-count":21,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Space It Up! Project funded by Italian Space Agency (ASI) and Italian Ministry of University and Research","award":["2024-5-E.0 (CUP No. I53D24000060005)"],"award-info":[{"award-number":["2024-5-E.0 (CUP No. I53D24000060005)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3629347","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:41:15Z","timestamp":1762368075000},"page":"191781-191794","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Electromagnetic Near-Field Scanning Using Physics-Informed Gaussian Process Regression"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7623-0781","authenticated-orcid":false,"given":"Tomas","family":"Monopoli","sequence":"first","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0564-6690","authenticated-orcid":false,"given":"Xinglong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6657-1530","authenticated-orcid":false,"given":"Sergio","family":"Amedeo Pignari","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1440-861X","authenticated-orcid":false,"given":"Johannes","family":"Wolf","sequence":"additional","affiliation":[{"name":"Power Systems, EMC and Space Environment Division, European Space Technology Center (ESTEC), European Space Agency (ESA), Noordwijk, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6844-8766","authenticated-orcid":false,"given":"Flavia","family":"Grassi","sequence":"additional","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MEMC.2023.10136447"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3015282"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TEMC.2007.902194"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TEMC.2011.2163821"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TEMC.2020.3025547"},{"key":"ref6","first-page":"3011","article-title":"Gaussian processes for machine learning (GPML) toolbox","volume":"11","author":"Rasmussen","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1494-6"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-15865-5"},{"key":"ref9","article-title":"Maximum likelihood estimation in Gaussian process regression is ill-posed","author":"Karvonen","year":"2022","journal-title":"arXiv:2203.09179"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"ref11","volume-title":"MATLAB: R2023a, The MathWorks, Inc., Natick, Massachusetts, 2023, Statistics and Machine Learning Toolbox, Gaussian Process Regression Functionality","year":"2023"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1137\/20M1315968"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2049662.2049669"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1002\/9781119115151"},{"key":"ref15","volume-title":"HFSS (High Frequency Structure Simulator) From ANSYS","year":"2025"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TEMC.2024.3492701"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TEMC.2023.3325242"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976907"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TEMC.2012.2207726"},{"key":"ref20","volume-title":"Probabilistic Machine Learning: An Introduction","author":"Murphy","year":"2022"},{"key":"ref21","article-title":"A tutorial on Bayesian optimization","author":"Frazier","year":"2018","journal-title":"arXiv:1807.02811"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11230079.pdf?arnumber=11230079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T18:51:28Z","timestamp":1763146288000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11230079\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3629347","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}