The International Council on Systems Engineering (INCOSE) has initiated a Future of Systems Engineering (FuSE) program that includes a stream for advancing the theoretical foundations of the discipline of Systems Engineering (SE). A near‐term goal of FuSE is to assess the adequacy of current theoretical foundations of SE. The discipline of SE is converging toward model‐based practices (i.e., MBSE) that have not yet reached the maturity of model‐based practices in other engineering domains. For example, finite element analysis and computational fluid dynamics are grounded in mathematical theory, while, generally, MBSE is not. However, some attempts have been made to underpin MBSE with theoretical richness. This article presents a systematic literature study that surveyed state of the art on providing MBSE with mathematical foundations. Our protocol collected over 2000 publications that were reviewed for inclusion/exclusion, categorized, and analyzed. We provide insights to the type of mathematical theories used, domains of applications, and areas of SE to which the math was applied to, among other analysis. We also provide a synthesized discussion about the field moving forward, emphasizing positive trends along with the negatives and areas of concern. Overall, we found the field to be nascent.

The paper suggests a method for analyzing technology trends. The process, which investigates development of technologies over time, identifies main technologies displaying the fastest growth compared to greater influence of new inventions. The method analyzes term frequency and change over time of technological terms in patents to identify the prior technologies that lead to a new technology and detects technologies that have the biggest impact. The analysis was performed on 4,354,054 patents from the US Patent Office dating from 1975 until today. Some correlation is displayed between technology trends and future US stock market performance.

The paper proposes new methods for knowledge prediction using network analytics and introduces pEgonet, sub-networks within knowledge networks consisting of to-beneighbors of new knowledge. Preliminary results show that it is feasible to predict how future knowledge is added in the knowledge network by utilizing basic properties of pEgonet. The paper presents initial work which will be expanded to derive a method to predict labelled future knowledge, with its impact and structures.