Data play an essential, important and fundamental role in the development of mechanics. The classical mechanics, represented by the three famous Newton’s laws, is developed by summarizing and analyzing vast data from the observations of natural behaviors, especially, from the observations of star movement. It is nowadays the information era and data grow explosively owing to the developments of high-throughput computation, high-throughput experiment and experimental automatization. Scientists in mechanics are facing great challenge: how to utilize the explosive growth of data to further develop mechanics, or how to gain
mechanics knowledge from big data. In this article, we propose to develop “mechanoinformatics” by employing techniques, tools, and theories drawn from the emerging fields of data science, internet, computer science and engineering, digital technologies, machine learning, and artificial intelligence (AI) to the field of mechanics to accelerate the development of mechanics. In “mechanoinformatics”, we emphasize the construction of mechanics databases and the combination of “human being learning” and “machine learning”. As examples, we sketchily elucidate symbolic regression, sparse regression and manifold learning methods, through which equations, theory and knowledge are achieved from data. Developing “mechanoinformatics” will definitely bring further prosperities to mechanics.
