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Graph."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            Manufactured parts are meticulously engineered to perform well with respect to several conflicting metrics, like weight, stress, and cost. The best achievable trade-offs reside on the\n            <jats:italic>Pareto front<\/jats:italic>\n            , which can be discovered via performance-driven optimization. The objectives that define this Pareto front often incorporate assumptions about the\n            <jats:italic>context<\/jats:italic>\n            in which a part will be used, including loading conditions, environmental influences, material properties, or regions that must be preserved to interface with a surrounding assembly. Existing multi-objective optimization tools are only equipped to study one context at a time, so engineers must run independent optimizations for each context of interest. However, engineered parts frequently appear in many contexts: wind turbines must perform well in many wind speeds, and a bracket might be optimized several times with its bolt-holes fixed in different locations on each run. In this paper, we formulate a framework for variable-context multi-objective optimization. We introduce the\n            <jats:italic>Pareto gamut<\/jats:italic>\n            , which captures Pareto fronts over a range of contexts. We develop a global\/local optimization algorithm to discover the Pareto gamut directly, rather than discovering a single fixed-context \"slice\" at a time. To validate our method, we adapt existing multi-objective optimization benchmarks to contextual scenarios. We also demonstrate the practical utility of Pareto gamut exploration for several engineering design problems.\n          <\/jats:p>","DOI":"10.1145\/3450626.3459750","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:26Z","timestamp":1626739466000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Pareto gamuts"],"prefix":"10.1145","volume":"40","author":[{"given":"Liane","family":"Makatura","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"Minghao","family":"Guo","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong"}]},{"given":"Adriana","family":"Schulz","sequence":"additional","affiliation":[{"name":"University of Washington"}]},{"given":"Justin","family":"Solomon","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"Wojciech","family":"Matusik","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/EVCO_a_00009"},{"key":"e_1_2_2_2_1","volume-title":"Suspension Tech: What is Anti-Squat? https:\/\/bikerumor.com\/2018\/09\/13\/suspension-tech-what-is-anti-squat\/","author":"Benedict Tyler","year":"2018","unstructured":"Tyler Benedict . 2018 . 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