On the value of test data for reducing uncertainty in material models: Computational framework and application to spherical indentation

Asaadi, Erfan; Heyns, P.S.; Haftka, Raphael T.; Tootkaboni, Mazdak

doi:10.1016/j.cma.2018.11.021

Cited by 7 publications

(1 citation statement)

References 55 publications

(117 reference statements)

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“…Some works that investigate the value of experimental data have been done. For instance, Asaadi et al (Asaadi et al 2019) presented a framework for addressing the influence of uncertainty sources and the value of the material response on the process of material model identification; and Hu et al (Hu et al 2017) proposed a calibration experiment design optimization to identify the optimal values of experimental inputs. These works could help engineers obtain the most useful experimental data under budget constraints.…”

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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Section: Introductionmentioning

confidence: 99%