Multifidelity Gaussian Process Model Integrating Low- and High-Fidelity Data Considering Censoring

Li, Min; Jia, Gaofeng

doi:10.1061/(asce)st.1943-541x.0002531

Cited by 11 publications

(3 citation statements)

References 41 publications

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“…Then, Raissi and Karniadakis (2016) combined the AR Co-kriging scheme with forward neural networks and proposed a deep multifidelity Gaussian process model, which could further solve nonlinear regression problems. Li and Jia (2020) proposed a general multifidelity Gaussian process model, which explicitly considered the censoring in high-fidelity data. Nevertheless, the multifidelity methods based on Gaussian process still have troubles in high-dimensional and strong nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Multifidelity approach for data‐driven prediction models of structural behaviors with limited data

Chen

Feng

2022

Computer aided Civil Eng

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The data‐driven approach based on plenty of high‐fidelity data such as experimental data becomes prevalent in the prediction of structural behavior. However, sometimes the high‐fidelity data are hard to obtain and are only in small amount. Meanwhile, the low‐fidelity data like simulation result are in large amount but their accuracy is relatively poor and are not suitable for establishing models. Thus, based on machine learning (ML) algorithms a multifidelity approach is present, which can enhance the prediction models performance under multifidelity data. First the basic theory and application procedure of this approach are introduced. Then a case study for predicting the shear capacity of reinforced concrete deep beams was carried out to validate this method's feasibility. The influence of different ML algorithms, low‐fidelity data resources, and high‐fidelity data ratios were thoroughly investigated. The results showed that this approach would effectively promote a models accuracy under multifidelity data and has the potential to be an alternative to facilitate solving some prediction issues in structural engineering.

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Section: Introductionmentioning

confidence: 99%

Multifidelity approach for data‐driven prediction models of structural behaviors with limited data

Chen

Feng

2022

Computer aided Civil Eng

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“…Various approaches have been proposed to build accurate surrogate models for the solutions of numerical structural analyses (e.g. for EDP estimation), always using a lower number of analyses (e.g 17–20 …”

Section: Introductionmentioning

confidence: 99%

“…for EDP estimation), always using a lower number of analyses (e.g. [17][18][19][20] ). Similarly, accurate surrogate models have been proposed for the seismic fragility parameters (i.e.…”

Section: Introductionmentioning

confidence: 99%

A multi‐fidelity Bayesian framework for robust seismic fragility analysis

Sevieri

Gentile

Galasso

2021

Earthq Engng Struct Dyn

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Fragility analysis of structures via numerical methods involves a complex trade‐off between the desired accuracy, the explicit consideration of uncertainties (both epistemic and aleatory) related to the numerical structural model and the available computational performance. This paper introduces a framework for deriving numerical fragility relationships based on multi‐fidelity non‐linear models of the structure under investigation and response‐analysis types. The proposed framework aims to reduce the computational burden while achieving a desired accuracy of the fragility estimates without neglecting aleatory and epistemic uncertainties. The proposed approach is an extension of the well‐known robust fragility (RF) analysis framework. Different model classes, each characterised by increasing refinement, are used to define multi‐fidelity polynomial expansions of the fragility model parameters. Each analysis result is then considered as a ‘new observation’ in a Bayesian framework and used to update the coefficients of the polynomial expansions. An adaptive sampling algorithm is also proposed to futher improve the performance of the multi‐fidelity framework. Specifically, such an adaptive sampling algorithm relies on partitioning the sample space and the Kullback–Leibler divergence to find the optimal sampling path. The sample space partitioning allows an analyst to specify different criteria and parameters of the algorithm for different regions, thus further improving the performance of the procedure. The proposed approach is illustrated for an archetype reinforced concrete (RC) frame for which two model classes are developed/analysed: the simple lateral mechanism analysis (SLaMA), coupled with the capacity spectrum method, and non‐linear dynamic analysis. Both model classes involve a cloud‐based approach employing unscaled real (i.e. recorded) ground motions. The fragility relationships derived with the proposed procedure are finally compared to those calculated by using only the most advanced/high‐fidelity (HF) model class, thus quantifying the performance of the proposed approach and highlighting further research needs.

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An efficient latent map multi-fidelity Kriging model and adaptive point-selected strategy for reliability analysis with time-consuming simulations

Tong,

Zhang,

Cui

et al. 2024

Struct Multidisc Optim

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Multifidelity Gaussian Process Model Integrating Low- and High-Fidelity Data Considering Censoring

Cited by 11 publications

References 41 publications

Multifidelity approach for data‐driven prediction models of structural behaviors with limited data

Multifidelity approach for data‐driven prediction models of structural behaviors with limited data

A multi‐fidelity Bayesian framework for robust seismic fragility analysis

An efficient latent map multi-fidelity Kriging model and adaptive point-selected strategy for reliability analysis with time-consuming simulations

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