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Machine Learning Drives Design Space Exploration: By combining simulation with probabilistic ML, engineers can chart the full design landscape, quantify uncertainty and uncover viable options that intuition and brute force alone would miss.

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Title: Machine Learning Drives Design Space Exploration: By combining simulation with probabilistic ML, engineers can chart the full design landscape, quantify uncertainty and uncover viable options that intuition and brute force alone would miss.
Source: Truck & Off-Highway Engineering. 12/1/2025, p10-13. 4p.
Subjects: Machine learning, Engineering simulations, Computer-aided engineering, Active learning, Finite element method
Abstract: The article focuses on how machine learning enhances engineering simulations to explore complex design spaces more efficiently. Topics include computer-aided engineering for high-dimensional systems, active learning to prioritize simulation runs, and the use of the finite element method to model and optimize vehicle components.
Database: Engineering Source
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  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 190644825
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
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  Data: Machine Learning Drives Design Space Exploration: By combining simulation with probabilistic ML, engineers can chart the full design landscape, quantify uncertainty and uncover viable options that intuition and brute force alone would miss.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+simulations%22">Engineering simulations</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+engineering%22">Computer-aided engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Active+learning%22">Active learning</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink>
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  Data: The article focuses on how machine learning enhances engineering simulations to explore complex design spaces more efficiently. Topics include computer-aided engineering for high-dimensional systems, active learning to prioritize simulation runs, and the use of the finite element method to model and optimize vehicle components.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190644825
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 4
        StartPage: 10
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Engineering simulations
        Type: general
      – SubjectFull: Computer-aided engineering
        Type: general
      – SubjectFull: Active learning
        Type: general
      – SubjectFull: Finite element method
        Type: general
    Titles:
      – TitleFull: Machine Learning Drives Design Space Exploration: By combining simulation with probabilistic ML, engineers can chart the full design landscape, quantify uncertainty and uncover viable options that intuition and brute force alone would miss.
        Type: main
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          Dates:
            – D: 01
              M: 12
              Text: 12/1/2025
              Type: published
              Y: 2025
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            – Type: issn-print
              Value: 24756148
          Titles:
            – TitleFull: Truck & Off-Highway Engineering
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