Rave: A Computational Framework to Facilitate Research in Design Decision Support

摘要

The cognitive challenges in the design of complex engineered systems include the scale and scope of decision problems, nonlinearity of the trade space, subjectivity of the problem formulation, and the need for rapid decision making. These challenges have motivated an active area of research in design decision-support methods and the development of commercial and openly available design frameworks. Although these frameworks are extremely capable, most are limiting as a basis for research relating to design decision support because they offer little user flexibility for incorporating and evaluating new features or techniques. This paper describes Rave, a computational framework designed specifically as a research platform for design decision-support methods. Rave has been structured to be flexible and adaptable, handle data with systematic data structures and descriptive metadata, facilitate a wide spectrum of visualization types, provide features to enable user interactivity and linking of graphics, and incorporate surrogate modeling and optimization as enabling capabilities. This framework is envisioned to provide the research and industrial communities an easily expandable and customizable baseline capability to facilitate investigation of further design decision-support advancements.