Active learning strategies for the estimation of a feasible set defined from a vector output black-box simulator Article Swipe
Clément Duhamel
,
Céline Helbert
,
Miguel Munoz Zuniga
,
Clémentine Prieur
,
Delphine Sinoquet
·
YOU?
·
· 2025
· Open Access
·
YOU?
·
· 2025
· Open Access
·
Many industrial problems require the use of black-box numerical simulators, for which it is essential to determine the set of so-called feasible input parameters. A set of parameters is feasible if the output of the code on these parameters satisfies given constraints, for example, by remaining below a certain threshold. Active learning is an effective approach to solve this type of problem, by sequentially enriching a design of experiments using a well-chosen acquisition criterion, based here on a Gaussian process surrogate model. In this work, we focus specifically on simulators with vector outputs. We propose several enrichment strategies to simultaneously explore the entire collection of feasible sets associated with each output component. These enrichment strategies are first tested and compared on analytical test functions, before being applied to the pre-calibration of a simulator dedicated to wind turbine design. The aim is to identify input parameter configurations that respect the vibration constraints imposed on the simulator outputs. Numerical results demonstrate the efficiency of the three proposed strategies.
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- Type
- preprint
- Language
- en
- Landing Page
- https://hal.science/hal-04970769
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414826822
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https://openalex.org/W4414826822Canonical identifier for this work in OpenAlex
- Title
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Active learning strategies for the estimation of a feasible set defined from a vector output black-box simulatorWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Clément Duhamel, Céline Helbert, Miguel Munoz Zuniga, Clémentine Prieur, Delphine SinoquetList of authors in order
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https://hal.science/hal-04970769Publisher landing page
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greenOpen access status per OpenAlex
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0Total citation count in OpenAlex
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