Model Calibration with Big Data

Cai, Guowei; Mahadevan, Sankaran

doi:10.1007/978-3-319-54858-6_31

Cited by 3 publications

(1 citation statement)

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“…Other approaches include parallel computing through divide‐and‐conquer methods (Cai & Mahadevan, 2017; Tsai et al, 2021), leveraging stochastic partial differential equations for addressing large nonstationary spatial outputs (Chang & Guillas, 2019), and subsampling techniques proposed by Lv et al (2023).…”

Section: Applications In Diverse Scenariosmentioning

confidence: 99%

A review on computer model calibration

Sung,

Tuo

2024

WIREs Computational Stats

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Model calibration is crucial for optimizing the performance of complex computer models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological advancement through the integration of advanced digital technologies into industrial processes, model calibration plays a key role in advancing digital twin technology, ensuring alignment between digital representations and real‐world systems. This comprehensive review focuses on the Kennedy and O'Hagan (KOH) framework (Kennedy and O'Hagan, Journal of the Royal Statistical Society: Series B 2001; 63(3):425–464). In particular, we explore recent advancements addressing the challenges of the unidentifiability issue while accommodating model inadequacy within the KOH framework. In addition, we explore recent advancements in adapting the KOH framework to complex scenarios, including those involving multivariate outputs and functional calibration parameters. We also delve into experimental design strategies tailored to the unique demands of model calibration. By offering a comprehensive analysis of the KOH approach and its diverse applications, this review serves as a valuable resource for researchers and practitioners aiming to enhance the accuracy and reliability of their computer models.This article is categorized under: Statistical Models > Semiparametric Models Statistical Models > Simulation Models Statistical Models > Bayesian Models

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Section: Applications In Diverse Scenariosmentioning

confidence: 99%