Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data, and then learn the combination and mapping patterns of these design features for the purposes of design solution retrieval, generation, optimization, evaluation, etc. Due to its capability of automatically and efficiently generating design solutions and thus supporting human-in-the-loop intelligent and innovative design activities, DICD has drawn the attentions from both academic and industrial fields. However, as an emerging research subject, there are still many unexplored issues that limit the development and application of DICD, such as specific dataset building, engineering design related feature engineering, systematic methods and techniques for DICD implementation in the entire product design process, etc. In this regard, a systematic and operable road map for DICD implementation from full-process perspective is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the computing mechanisms for DICD implementation, key enabling technologies for detailed DICD implementation, and three application scenarios of DICD. The road map reveals the common mechanisms and calculation principles of existing DICD researches, and thus it can provide systematic guidance for the possible DICD applications that have not been explored.

翻译：数据驱动智能计算设计（DICD）是在人工智能快速发展的背景下涌现的研究热点。它强调利用深度学习算法提取和表示隐藏在历史或虚构设计过程数据中的设计特征，进而学习这些设计特征的组合与映射模式，以实现设计方案检索、生成、优化、评估等目标。由于DICD能够自动、高效地生成设计方案，从而支持人在环的智能与创新设计活动，因此受到了学术界和工业界的广泛关注。然而，作为新兴研究课题，仍存在许多未探索的问题限制了DICD的发展与应用，例如特定数据集构建、工程设计相关的特征工程、面向全产品设计流程的系统性方法与技术等。为此，本文建立了从全流程视角出发的系统性、可操作的DICD实施路线图，包括DICD项目规划的一般工作流、DICD项目实施的总体框架、DICD实施的计算机制、DICD详细实施的关键使能技术，以及DICD的三种应用场景。该路线图揭示了现有DICD研究的通用机制与计算原理，从而能为尚未探索的潜在DICD应用提供系统性指导。