A novel learning function for adaptive surrogate-model-based reliability evaluation.

The classical reliability analysis methods, due to the ever-increasing complexity of engineering structure, may lead to higher and higher calculation errors and costs. The adaptive surrogate-model-based reliability evaluation method strikes a desirable balance between computational efficiency and accuracy, making it a prevalent technique in the domain of reliability evaluation. Learning function is the core of this reliability evaluation method.

Interaction prediction optimization in multidisciplinary design optimization problems.

The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem.