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:数据驱动的智能计算型设计(DICD) 是在快速发展的人工智能背景下出现的研究热点。它强调利用深度学习算法提取并表示历史或制定的设计流程数据中隐含的设计特征,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。由于DICD可以自动高效地生成设计方案,因此支持人为导向的智能和创新设计活动,受到了学术和工业领域的关注。但是,作为一个新兴的研究方向,目前还存在许多未探索的问题,如特定数据集构建、工程设计相关的特征工程、DICD实施于整个产品设计过程的系统方法和技术等。因此,从全过程的角度建立了一个DICD实施的系统和可操作的路线图,包括DICD项目规划的一般工作流程,DICD项目实施的整体框架,DICD实施的计算机制,具体DICD实施的关键技术以及三种DICD应用场景。该路线图揭示了现有 DICD 研究的常见机制和计算原则,因此可以为未被探索的 DICD 应用提供系统的指导。