The main purpose of modeling is to translate physics into a simplified representation, including just the significant variables that determine most of the emergence output. Systems Modeling has been a useful tool to develop complex systems and new technologies. On the other hand, systems modeling has constraints to generate representative models with multiple dynamic and nonlinear variables, therefore there is an incremental gap between the models’ capabilities and the growing rate of systems’ complexity. This gap is generated by the lack of smart modeling tools to identify the critical factors that generate most of the system’s results, under increasing disturbing environments.

The development of Smart Systems Modeling (SSM), powered by Artificial Intelligence (AI), is an evolution of the Smart Model Based Systems Engineering (SMBSE)* approach, to bridge the gap between modeling and the increasingly complex systems by the development of Systems Engineering models, enhanced by automation, optimization, reusability and dynamic simulation functionalities. AI available tools allow the transformation of SMBSE into more powerful models, enhanced with simultaneous modeling and virtual simulation capabilities, as well as regression techniques to predict results and the required flexibility to adequate the core model for new functions and interfaces.

AI enhances SSM with smart functionalities to determine patterns of relationship and the significant variables across complex big data and non-structured inputs. AI-Machine Learning (ML) will support the development of SSM with learning abilities to improve the models’ fidelity until an acceptable correlation level is achieved. This is just the beginning of a promising growing solution to develop powerful design and research models, in conjunction with AI’s decision-making processes and learning capabilities. The SSM progress will provide benefits for faster replacement of physical tests by robust virtual model simulation, for new products development, as well as agile research experiments.