A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
arXiv preprint arXiv:1609.07196 , year=
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A review of multi-fidelity surrogates from co-kriging to neural networks for composite mechanics, with applications in prediction, optimization, and workflow integration.
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
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Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
A review of multi-fidelity surrogates from co-kriging to neural networks for composite mechanics, with applications in prediction, optimization, and workflow integration.