Multi-Fidelity Surrogate Based on Single Linear Regression
Various frameworks have been proposed to predict mechanical system responses by combining data from different fidelities for design optimization and uncertainty quantification as reviewed by Fernández-Godino et al. and Peherstorfer et al.. Among all frameworks, the Bayesian framework based on Gaussian processes has the potential of highest accuracy. However, the Bayesian framework requires optimization for estimating hyper-parameters, and there is a risk of estimating inappropriate hyper-parameters as Kriging surrogate often does, especially in the presence of noisy data. We propose an easy and yet powerful framework for practical design and applications. In this technical note, we revised a heuristic framework which minimizes the prediction errors at high-fidelity samples using optimization. The system behavior (high-fidelity behavior) is approximated by a linear combination of the low-fidelity predictions and a polynomial-based discrepancy function. The key idea is to consider the low-fidelity model as a basis function in the multi-fidelity model with the scale factor as a regression coefficient. The design matrix for least-square estimation consists of both the low-fidelity model and discrepancy function. Then the scale factor and coefficients of the basis functions are obtained simultaneously using linear regression, which guarantees the uniqueness of fitting process. Besides enabling efficient estimation of the parameters, the proposed least-squares multi-fidelity surrogate (LS-MFS) can be applicable to other regression models by simply replacing the design matrix. Therefore, the LS-MFS is expected to be easily applied to various applications such as prediction variance, D-optimal designs, uncertainty propagation and design optimization.