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The desired properties being the target bandgap width and bandgap midfrequency, the design model proposes candidate unit cell topologies. The methodology demonstrates exceptional accuracy in both the forward prediction and inverse design of 2-dimensional metamaterial structures, with particular emphasis on bandgap characteristics. Statistical analysis reveals\n            <jats:italic toggle=\"yes\">R<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            coefficients exceeding 0.99, validating the model\u2019s predictive capabilities. The demonstrated framework represents a substantial advancement in computational metamaterial design, offering potential applications across multiple materials science and engineering domains.\n          <\/jats:p>","DOI":"10.34133\/icomputing.0139","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:39:24Z","timestamp":1752230364000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":6,"title":["On-Demand Inverse Design of Metamaterials Using Deep Neural Networks with Bayesian Optimization"],"prefix":"10.34133","volume":"4","author":[{"given":"Than V.","family":"Tran","sequence":"first","affiliation":[{"name":"Institute of Photonics, \rLeibniz University Hannover, Hannover 30167, Germany."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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