Computational infrastructure for concepts discovery in science and technology |
| |
| Affiliation: | 1. PHM Laboratory, Department of Mechanical Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva 8410501, Israel;2. Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, United States;1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;2. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;3. Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China |
| |
| Abstract: | Automated scientific discovery, a topic in artificial intelligence has mainly been used to generate scientific insight from data. Our work follows the knowledge-driven discovery approach and introduces the use of category theory as the foundation for modeling diverse engineering fields represented with combinatorial representation. We show how category theory provides support for all stages of the discovery process starting from modeling the engineering knowledge. We demonstrate the use of the approach to rediscover previous discoveries in mechanics and discover new devices, some of which need to be realized to be appreciated. Category theory allows expanding the process to disciplines not modeled with combinatorial representations. We intend to demonstrate this in future studies. |
| |
| Keywords: | Duality Category theory IEKG Combinatorial representations Knowledge-driven discovery |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
