{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:40:12Z","timestamp":1763545212565,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EPSRC animate grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The design of a metasurface array consisting of different unit cells with the objective of minimizing its radar cross-section is a popular research topic. Currently, this is achieved by conventional optimisation algorithms such as genetic algorithm (GA) and particle swarm optimisation (PSO). One major concern of such algorithms is the extreme time complexity, which makes them computationally forbidden, particularly at large metasurface array size. Here, we apply a machine learning optimisation technique called active learning to significantly speed up the optimisation process while producing very similar results compared to GA. For a metasurface array of size 10 \u00d7 10 at a population size of 106, active learning took 65 min to find the optimal design compared to genetic algorithm, which took 13,260 min to return an almost similar optimal result. The active learning optimisation strategy produced an optimal design for a 60 \u00d7 60 metasurface array 24\u00d7 faster than the approximately similar result generated by GA technique. Thus, this study concludes that active learning drastically reduces computational time for optimisation compared to genetic algorithm, particularly for a larger metasurface array. Active learning using an accurately trained surrogate model also contributes to further lowering of the computational time of the optimisation procedure.<\/jats:p>","DOI":"10.3390\/s23125546","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:01:40Z","timestamp":1686708100000},"page":"5546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Active Learning Optimisation of Binary Coded Metasurface Consisting of Wideband Meta-Atoms"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6671-7579","authenticated-orcid":false,"given":"Parvathy","family":"Chittur Subramanianprasad","sequence":"first","affiliation":[{"name":"School of Electronics Engineering and Computer Science, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4NS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2411-1867","authenticated-orcid":false,"given":"Yihan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering and Computer Science, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4NS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4250-187X","authenticated-orcid":false,"given":"Achintha Avin","family":"Ihalage","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering and Computer Science, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4NS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9949-7226","authenticated-orcid":false,"given":"Yang","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering and Computer Science, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4NS, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1039\/c0cs00184h","article-title":"Metamaterials: A new frontier of science and technology","volume":"40","author":"Liu","year":"2011","journal-title":"Chem. 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