Biologically inspired design perhaps is one of the most important movements in engineering design. The paradigm espouses use of analogies to biology in generating conceptual designs for new technologies. In this paper, we briefly summarize some empirical findings about biologically inspired design, and then develop an information-processing theory of creative analogies in biologically inspired design. We also compare our theory with similar theories. In addition, we examine how biologically inspired design is fundamentally different from other design paradigms.

An important issue in teaching interdisciplinary biologically inspired design is the external representations we use to foster understanding of biological systems. In this study we explore if functional models of biological systems, and in particular Structure-Behavior-Function (SBF) models, enable humans to better understand complex biological systems. The study compares the use of SBF models in answering questions about biological systems versus the use of textual, tabular and graphical representations. The results indicate that while no one representation is best for answering all types of questions, SBF models enable more accurate answers to questions entailing abstract and complex inferences.

The task of biologically inspired design requires designers to access, understand, and apply knowledge of biological systems. One common source for obtaining this kind of knowledge is scholarly biology articles accessed from online libraries and bibliographic databases (e.g., Web of Science, Google Scholar). However, our studies show that such online information environments do not adequately support this kind of interdisciplinary research. Designers need more help with both accessing relevant biology articles and understanding the biological systems that are described in those articles. In this paper, we present Biologue, a social citation cataloging system that uses model-based tagging to address these challenges.

We describe a technique that takes conceptual, declarative Structure-Behavior-Function (SBF) models of a complex system, and simulates the behavior of the system in Net Logo, an agent-based simulation environment. Our technique uses a library of behavior patterns, where a behavior pattern is a parameterized generic abstraction over classes of SBF models. Given an SBF model constructed by a student, our technique first recognizes the model as an instance of a behavior pattern, then uses the parameters of the pattern to set up the Net Logo simulation, and finally runs the simulation to produce an animation that acts as feedback on the conceptual model.

Biologically inspired design uses cross-domain analogies from biology to engineering to enhance design creativity and innovation. This analogical transfer requires conceptual understanding of biological systems. In this paper, we describe a prototype interactive knowledge-based design environment called DANE for supporting conceptual understanding through learning about functional models of biological designs. We present initial results from deploying DANE in an interdisciplinary class on biologically inspired design, indicating that designers found DANE's functional models useful for conceptualizing complex systems.

Biologically inspired design is an important emerging movement in engineering design. Finding relevant biological sources of inspiration from existing biology literature is one of the important challenges of this activity. We conjecture that annotating biology articles with lightweight Structure-Behavior-Function (SBF) models is one way to address this challenge. We present Biologue, a social citation cataloging system that allows its users to gather, organize, share, and most importantly, annotate scholarly articles with SBF models. This feature not only allows the implementation of search mechanism that is more targeted to the needs of designers seeking bio-inspiration, but also helps designers make sense of the articles returned by the search mechanism.

The core problem we address in this paper is how to take a declarative conceptual representation of a complex system and produce an agent-based simulation of that model. In particular, we describe a computational technique that takes Structure-Behavior-Function models of complex systems and simulates the behavior of the modeled system in NetLogo, an agent-based programming and simulation environment. This technique has been implemented in an interactive learning environment to promote complex systems learning among middle school students.

Analogy is a fundamental process of creativity. Biologically inspired design by definition entails cross-domain analogies, and in practice has led to many innovative designs. Thus, biological inspired design is an ideal domain for studying creative analogies. In this paper, we describe an intricate episode of biologically inspired design that unfolded over an extended period of time. We then analyze the episode in terms of Why, What, How and When questions of analogy. This analysis provides a content theory of creative analogies in biologically inspired design.

In this paper, we compare three methods for forming reduced models to speed up genetic algorithm (GA) based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization by making the genetic operators more informed. Empirical results in several engineering design domains are presented.