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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

STOCHASTIC MULTIOBJECTIVE OPTIMIZATION ON A BUDGET: APPLICATION TO MULTIPASS WIRE DRAWING WITH QUANTIFIED UNCERTAINTIES

Volume 8, Edição 3, 2018, pp. 233-249
DOI: 10.1615/Int.J.UncertaintyQuantification.2018021315
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RESUMO

Design optimization of engineering systems with multiple competing objectives is a painstakingly tedious process especially when the objective functions are expensive-to-evaluate computer codes with parametric uncertainties. The effectiveness of the state-of-the-art techniques is greatly diminished because they require a large number of objective evaluations, which makes them impractical for problems of the above kind. Bayesian global optimization (BGO) has managed to deal with these challenges in solving single-objective optimization problems and has recently been extended to multiobjective optimization (MOO). BGO models the objectives via probabilistic surrogates and uses the epistemic uncertainty to define an information acquisition function (IAF) that quantifies the merit of evaluating the objective at new designs. This iterative data acquisition process continues until a stopping criterion is met. The most commonly used IAF for MOO is the expected improvement over the dominated hypervolume (EIHV) which in its original form is unable to deal with parametric uncertainties or measurement noise. In this work, we provide a systematic reformulation of EIHV to deal with stochastic MOO problems. The primary contribution of this paper lies in being able to filter out the noise and reformulate the EIHV without having to observe or estimate the stochastic parameters. An addendum of the probabilistic nature of our methodology is that it enables us to characterize our confidence about the predicted Pareto front. We verify and validate the proposed methodology by applying it to synthetic test problems with known solutions. We demonstrate our approach on an industrial problem of die pass design for a steel-wire drawing process.

CITADO POR
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  2. Naik Pratik, Pandita Piyush, Aramideh Soroush, Bilionis Ilias, Ardekani Arezoo M., Bayesian model calibration and optimization of surfactant-polymer flooding, Computational Geosciences, 23, 5, 2019. Crossref

  3. Zhang J., Taflanidis A. A., Evolutionary Multi-Objective Optimization Under Uncertainty Through Adaptive Kriging in Augmented Input Space, Journal of Mechanical Design, 142, 1, 2020. Crossref

  4. Shu Leshi, Jiang Ping, Shao Xinyu, Wang Yan, A New Multi-Objective Bayesian Optimization Formulation With the Acquisition Function for Convergence and Diversity, Journal of Mechanical Design, 142, 9, 2020. Crossref

  5. Galuzio Paulo Paneque, de Vasconcelos Segundo Emerson Hochsteiner, Coelho Leandro dos Santos, Mariani Viviana Cocco, MOBOpt — multi-objective Bayesian optimization, SoftwareX, 12, 2020. Crossref

  6. Ehrett Carl, Brown D. Andrew, Chodora Evan, Kitchens Christopher, Atamturktur Sez, Multi-Objective Engineering Design Via Computer Model Calibration, Journal of Mechanical Design, 143, 5, 2021. Crossref

  7. Pandita Piyush, Tsilifis Panagiotis, Awalgaonkar Nimish M., Bilionis Ilias, Panchal Jitesh, Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian Processes, Computer Methods in Applied Mechanics and Engineering, 385, 2021. Crossref

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  9. Tsilifis Panagiotis, Pandita Piyush, Ghosh Sayan, Wang Liping, Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds, Entropy, 24, 9, 2022. Crossref

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