Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design
Abstract The adoption of high-fidelity models in multidisciplinary design optimization (MDO) permits to enhance the identification of superior design configurations, but would prohibitively rise the demand for computational resources and time. Multifidelity Bayesian Optimization (MFBO) efficiently c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-48757-3 |
| _version_ | 1827248628657291264 |
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| author | Francesco Di Fiore Laura Mainini |
| author_facet | Francesco Di Fiore Laura Mainini |
| author_sort | Francesco Di Fiore |
| collection | DOAJ |
| description | Abstract The adoption of high-fidelity models in multidisciplinary design optimization (MDO) permits to enhance the identification of superior design configurations, but would prohibitively rise the demand for computational resources and time. Multifidelity Bayesian Optimization (MFBO) efficiently combines information from multiple models at different levels of fidelity to accelerate the MDO procedure. State-of-the-art MFBO methods currently meet two major limitations: (i) the sequential adaptive sampling precludes parallel computations of high-fidelity models, and (ii) the search scheme measures the utility of new design evaluations only at the immediate next iteration. This paper proposes a Non-Myopic Multipoint Multifidelity Bayesian Optimization (NM3-BO) algorithm to sensitively accelerate MDO overcoming the limitations of standard methods. NM3-BO selects a batch of promising design configurations to be evaluated in parallel, and quantifies the expected long-term improvement of these designs at future steps of the optimization. Our learning scheme leverages an original acquisition function based on the combination of a two-step lookahead policy and a local penalization strategy to measure the future utility achieved evaluating multiple design configurations simultaneously. We observe that the proposed framework permits to sensitively accelerate the MDO of a space vehicle and outperforms popular algorithms. |
| first_indexed | 2024-03-08T19:47:32Z |
| format | Article |
| id | doaj.art-8c5736237c0547b99453a362d571f992 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2025-03-21T23:34:46Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8c5736237c0547b99453a362d571f9922024-05-19T11:20:53ZengNature PortfolioScientific Reports2045-23222023-12-0113111310.1038/s41598-023-48757-3Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary designFrancesco Di Fiore0Laura Mainini1Departement of Mechanical and Aerospace Engineering, Politecnico di TorinoDepartement of Mechanical and Aerospace Engineering, Politecnico di TorinoAbstract The adoption of high-fidelity models in multidisciplinary design optimization (MDO) permits to enhance the identification of superior design configurations, but would prohibitively rise the demand for computational resources and time. Multifidelity Bayesian Optimization (MFBO) efficiently combines information from multiple models at different levels of fidelity to accelerate the MDO procedure. State-of-the-art MFBO methods currently meet two major limitations: (i) the sequential adaptive sampling precludes parallel computations of high-fidelity models, and (ii) the search scheme measures the utility of new design evaluations only at the immediate next iteration. This paper proposes a Non-Myopic Multipoint Multifidelity Bayesian Optimization (NM3-BO) algorithm to sensitively accelerate MDO overcoming the limitations of standard methods. NM3-BO selects a batch of promising design configurations to be evaluated in parallel, and quantifies the expected long-term improvement of these designs at future steps of the optimization. Our learning scheme leverages an original acquisition function based on the combination of a two-step lookahead policy and a local penalization strategy to measure the future utility achieved evaluating multiple design configurations simultaneously. We observe that the proposed framework permits to sensitively accelerate the MDO of a space vehicle and outperforms popular algorithms.https://doi.org/10.1038/s41598-023-48757-3 |
| spellingShingle | Francesco Di Fiore Laura Mainini Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design Scientific Reports |
| title | Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design |
| title_full | Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design |
| title_fullStr | Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design |
| title_full_unstemmed | Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design |
| title_short | Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design |
| title_sort | non myopic multipoint multifidelity bayesian framework for multidisciplinary design |
| url | https://doi.org/10.1038/s41598-023-48757-3 |
| work_keys_str_mv | AT francescodifiore nonmyopicmultipointmultifidelitybayesianframeworkformultidisciplinarydesign AT lauramainini nonmyopicmultipointmultifidelitybayesianframeworkformultidisciplinarydesign |