Gradient-enhanced variable-fidelity proxy model modeling method

Abstract

The invention discloses a gradient-enhanced variable-fidelity proxy model modeling method, and belongs to the technical field of proxy models. The method comprises the following steps: (1) establishing a low-fidelity model according to a low-fidelity sample and a low-fidelity real response and a low-fidelity real gradient corresponding to the low-fidelity sample; taking a gradient-enhanced radial basis function model as a low-fidelity model, and giving a low-fidelity prediction response and a low-fidelity prediction gradient at a high-fidelity sample through the established low-fidelity model; (2) introducing a scale factor and a difference function to correct the low-fidelity model; (3) solving a scale factor and an undetermined coefficient column vector in the difference function; and (4) establishing a prediction expression of the gradient-enhanced variable fidelity proxy model. According to the method, the physical significance of gradient information can be fully considered on the basis of a traditional variable fidelity proxy model, so that the prediction precision of the model is further enhanced. Meanwhile, Cholesky decomposition is used when inversion is carried out on the established correlation matrix, and the calculation efficiency can be effectively guaranteed.

Description

technical field [0001] The invention belongs to the technical field of proxy models, and relates to a modeling method of a gradient-enhanced variable-fidelity proxy model. Background technique [0002] The surrogate model establishes a mathematical approximation for the relationship between the design variables and the response of the complex system, focusing on solving the problems of excessive calculation in engineering design or the inability of existing computing resources to meet the computing needs. It has been used in different Scientific fields (such as design optimization, material design, reliability analysis, digital twins, and uncertainty quantification, etc.) have received extensive attention and applications. In addition, surrogate models can be of great help in dealing with noisy or missing data and in gaining insight into complex functional relationships between design variables and output responses. [0003] Although the surrogate model method saves a lot o...

AI Technical Summary

Problems solved by technology

However, current gradient-boosted variable-fidelity proxy models only use gradient information as a sample response to improve model accuracy, without fully considering its physical meanin

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