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This work addresses rapid inverse design of such metamaterials to meet target qualitative functional behaviors, a challenge due to its intractability and nonunique solutions. Unlike data\u2010intensive and noninterpretable deep\u2010learning\u2010based methods, this work proposes the random\u2010forest\u2010based interpretable generative inverse design (RIGID), a single\u2010shot inverse design method for fast generation of metamaterials with on\u2010demand functional behaviors. RIGID leverages the interpretability of a random forest\u2010based \u201cdesign\u2009\u2192\u2009response\u201d forward model, eliminating the need for a more complex \u201cresponse\u2009\u2192\u2009design\u201d inverse model. Based on the likelihood of target satisfaction derived from the trained random forest, one can sample a desired number of design solutions using Markov chain Monte Carlo methods. RIGID is validated on acoustic and optical metamaterial design problems, each with fewer than 250 training samples. Compared to the genetic algorithm\u2010based design generation approach, RIGID generates satisfactory solutions that cover a broader range of the design space, allowing for better consideration of additional figures of merit beyond target satisfaction. This work offers a new perspective on solving on\u2010demand inverse design problems, showcasing the potential for incorporating interpretable machine learning into generative design under small data constraints.<\/jats:p>","DOI":"10.1002\/aisy.202400611","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T15:24:51Z","timestamp":1730993091000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3807-1862","authenticated-orcid":false,"given":"Wei (Wayne)","family":"Chen","sequence":"first","affiliation":[{"name":"J. 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