Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 60, Issue 4, Pages 1619-1644Publisher
SPRINGER
DOI: 10.1007/s00158-019-02270-2
Keywords
Forward problem; Inverse problem; Validation problem; Uncertainty quantification; Statistical model calibration; Validity check
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Funding
- Institute of Advanced Machinery and Design at Seoul National University (SNU-IAMD)
- Korea Institute of Machinery and Materials [NK213E]
- National Research Council of Science & Technology (NST), Republic of Korea [NK213E] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Computer-aided engineering (CAE) is now an essential instrument that aids in engineering decision-making. Statistical model calibration and validation has recently drawn great attention in the engineering community for its applications in practical CAE models. The objective of this paper is to review the state-of-the-art and trends in statistical model calibration and validation, based on the available extensive literature, from the perspective of uncertainty structures. After a brief discussion about uncertainties, this paper examines three problem categories-the forward problem, the inverse problem, and the validation problem-in the context of techniques and applications for statistical model calibration and validation.
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