小样本条件下智能布点代理模型及优化设计

Aiming at the problem that traditional typical surrogate models need to use a large number of sample points to achieve high-precision global prediction, and the consumption of time and computing resources is difficult to meet the needs of engineering and scientific computing, according to the optimization design depending on the surrogate model and optimization algorithm, an intelligent placement model is proposed for efficient forecasting under small sample conditions. Considering that for optimization, only a high-precision surrogate model near the Pareto frontier (local) needs to be constructed, the intelligent distribution method was improved and realized through the idea of the trust region, the sampling space and the surrogate model was updated according to the needs of the prediction accuracy, which improves the accuracy of the prediction. Taking a small amorphous alloy transformer as a verification case, the intelligent distribution surrogate model and the traditional typical orthogonal experimental method were compared under only 70 sets of sample data. The results show that the prediction accuracy of the intelligent distribution surrogate model is improved by about 16% compared with the traditional proxy model, which verifies the effective modeling and prediction accuracy of the surrogate model in the case of few samples, and conducts an optimized design.