Pareto Gamuts : exploring optimal designs across varying contexts
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/129366 |
| _version_ | 1826192977591009280 |
|---|---|
| author | Makatura, Liane. |
| author2 | Wojciech Matusik. |
| author_facet | Wojciech Matusik. Makatura, Liane. |
| author_sort | Makatura, Liane. |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 |
| first_indexed | 2024-09-23T09:31:45Z |
| format | Thesis |
| id | mit-1721.1/129366 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:31:45Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1293662021-01-12T03:29:32Z Pareto Gamuts : exploring optimal designs across varying contexts Exploring optimal designs across varying contexts Makatura, Liane. Wojciech Matusik. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 69-73). 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 Pareto front, which can be discovered via performance-driven optimization. Objective functions used to define the Pareto front often incorporate assumptions about the context 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 Pareto gamut, 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. by Liane Makatura. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2021-01-11T17:19:51Z 2021-01-11T17:19:51Z 2020 2020 Thesis https://hdl.handle.net/1721.1/129366 1227278307 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 73 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Makatura, Liane. Pareto Gamuts : exploring optimal designs across varying contexts |
| title | Pareto Gamuts : exploring optimal designs across varying contexts |
| title_full | Pareto Gamuts : exploring optimal designs across varying contexts |
| title_fullStr | Pareto Gamuts : exploring optimal designs across varying contexts |
| title_full_unstemmed | Pareto Gamuts : exploring optimal designs across varying contexts |
| title_short | Pareto Gamuts : exploring optimal designs across varying contexts |
| title_sort | pareto gamuts exploring optimal designs across varying contexts |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/129366 |
| work_keys_str_mv | AT makaturaliane paretogamutsexploringoptimaldesignsacrossvaryingcontexts AT makaturaliane exploringoptimaldesignsacrossvaryingcontexts |