Engineering design is a complex process to find a suitable trade-off among different, and sometimes conflicting, design specifications. In reality, these requirements can be often considered as constraints of the design problem, that can be defined in terms of performance measures or geometrical characteristics of the device under study. In this paper, a new design space exploration methodology is presented for discovering feasible regions in the design space, where the term feasible region indicates the set of all design configurations satisfying all constraints of the design problem. The proposed method is based on Gaussian process metamodels to estimate the feasible region and leverages a information-based adaptive sampling technique to sequentially refine the prediction accuracy, which is applicable for multiple constraints problems. To efficiently stop the adaptive sampling process, a novel framework to estimate the metamodel’s prediction accuracy is proposed. The efficiency, accuracy and robustness of the proposed approach are compared with state-of-art techniques on suitable benchmark problems and practical engineering examples.

工程设计是一个复杂的过程，需要在不同的，有时是相互冲突的设计规范之间找到适当的取舍。实际上，这些要求通常可以视为设计问题的约束条件，可以根据研究中的设备的性能指标或几何特性来定义这些要求。本文提出了一种新的设计空间探索方法，用于发现设计空间中的可行区域，其中术语“可行区域”表示满足设计问题所有约束的所有设计配置的集合。所提出的方法基于高斯过程元模型来估计可行区域，并利用基于信息的自适应采样技术来顺序地改进预测精度，这适用于多约束问题。为了有效地停止自适应采样过程，提出了一种新颖的框架来估计元模型的预测精度。在合适的基准问题和实际工程实例上，将所提出的方法的效率，准确性和鲁棒性与最新技术进行了比较。