PeTaL (Periodic Table of Life) and Physiomimetics

The Periodic Table of Life (PeTaL) is a system design tool and open source framework that uses artificial intelligence (AI) to aid in the systematic inquiry of nature for its application to human systems. This paper defines PeTaL’s architecture and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields has been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. An objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence, topology optimization and conventional modeling in problem solving. Specifically, specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools that support PeTaL include machine learning, natural language processing and computer vision. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life. Also discussed is the formation of V.I.N.E. (Virtual Interchange for Nature-inspired Exploration), a virtual collaborative aimed at generating data, research and applications centered on nature. Details of implementation will be presented in subsequent publications. Recommendations for future work are also presented.