中文摘要：

为设计满足特定目标并具有近似自然形态的多孔隙结构，同时实现对结构内部连通性的控制，提出一种基于样本学习的多孔隙结构设计方法．首先以多孔介质的孔隙率和连通性作为设计指标，以6-邻接体素结构建立参数化设计单元，通过手工标记方式建立设计单元样本库；在此基础上，利用随机决策树森林（RDF）学习样本结构与设计目标之间的关联模型，生成设计规则；最后将整个设计方法集成于一个可扩展的多孔隙建模框架中．实验结果表明，RDF的泛化能力能够对样本库之外的结构进行正确的判定，可以在满足设计目标的同时得到更加自然的多孔隙结构．

英文摘要：

In order to design porous models with specific functions, good internal connectivity and natural structures, this paper proposes a new learning-based porous structure modeling method. Firstly, the porosity and connectivity are selected as the evaluation indices. Then a structure with 6- adjacent voxel is designed as the parametric design unit, which is used to establish a manually labeled training database. Random decision forest （RDF） is utilized to learn the correlation model between sample structures and design targets. This correlation model is finally integrated into a scalable porous structure modeling framework. Experimental results show that the generalization capability of the RDF is able to give correctly judgments for those structures beyond the training data, which makes it possible to generate more natural porous structure to satisfy certain design goals.