A systematic approach for model refinement considering blind and recognized uncertainties in engineered product development

Oh, Hyunseok; Kim, Jisun; Son, Hyejeong; Youn, Byeng D.; Jung, Byung C.

doi:10.1007/s00158-016-1493-3

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…, β l }; s and l are the number of the operation and model variables, respectively; and e and δ are the measurement and modeling errors, respectively. In HTVC, e and δ are considered to be encompassed in y and ψ, unless the true functions of e and δ are quantified explicitly [18]. The effect of bad and missing data on classification problems was discussed in [13].…”

Section: Procedures Of Experimental Designmentioning

confidence: 99%

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

et al. 2020

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For model verification and validation (V & V) in computational mechanics, a hypothesis test for the validity check (HTVC) is useful, in particular, with a limited number of experimental data. However, HTVC does not address how type I and II errors can be reduced when additional resources for sampling become available. For the validation of computational models of safety-related and mission-critical systems, it is challenging to design experiments so that type II error is reduced while maintaining type I error at an acceptable level. To address the challenge, this paper proposes a new method to design validation experiments, response-adaptive experiment design (RAED). The RAED method adaptively selects the next experimental condition from among candidates of various operating conditions (experimental settings). RAED consists of six key steps: (1) define experimental conditions, (2) obtain experimental data, (3) calculate u-values, (4) compute the area metric, (5) select the next experimental condition, and (6) obtain additional experimental datum. To demonstrate the effectiveness of the RAED method, a case study of a numerical example is shown. It is demonstrated that additional experimental data obtained through the RAED method can reduce type II error in hypothesis testing and increase the probability of rejecting an invalid computational model.

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Section: Procedures Of Experimental Designmentioning

confidence: 99%

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

et al. 2020

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“…Model refinement and model updating are two available ways to improve the model accuracy (Xiong et al 2009;Youn et al 2011;Oh et al 2016). Model refinement refers to changing the physical principles or utilizing sophisticated modeling methods to build a more precise model.…”

Section: Introductionmentioning

confidence: 99%

A new model updating strategy with physics-based and data-driven models

Xiang

Pan

Luo

2021

Struct Multidisc Optim

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For engineering simulation models, insufficient experimental data and imperfect understanding of underlying physical principles often make predictive models inaccurate. It is difficult to reduce the model bias effectively with limited information. To improve the predictive performances of the models, this paper proposes a new model updating strategy utilizing a data-driven model to integrate with a physics-based model. One of the main strengths of the proposed method is that it maximizes the utilization of existing limited information by combining physics-based and data-driven models built based on different principles. First, the physics-based model is updated via selecting a suitable updating method and updating formulation. A data-driven model is then constructed using the Gaussian process (GP) regression. Finally, a weight combination is employed to obtain the updated predictive model where the weights of experimental sites and non-experimental sites are determined by the minimum discrepancy of probability distributions of the posterior error and another data-driven model, respectively. The Sandia thermal challenge problem is used to demonstrate the effectiveness of the proposed method.

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Review of statistical model calibration and validation—from the perspective of uncertainty structures

Lee

Kim

et al. 2019

Struct Multidisc Optim

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A systematic approach for model refinement considering blind and recognized uncertainties in engineered product development

Cited by 8 publications

References 27 publications

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

A new model updating strategy with physics-based and data-driven models

Review of statistical model calibration and validation—from the perspective of uncertainty structures

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