Making simulations future proof

Abstract

Abraham Lincoln stated that ‘‘The best way to predict your future is to create it.’’ So, in our simulation market of the future, I am creating the future of simulations. First, as it has been stated over the last 30 years, modeling and simulation (M&S) is great and will change our futures in so many ways. One need only look on the Internet and will find areas of M&S that we thought were not relevant to simulation, now abounding with simulation-based training, processes, and learning techniques. For example, the Healthcare Industry has really exploded in the last decade in applying simulations for healthcare training and education. Computer chips are reaching the dead end in 2025 to fulfill Moore’s Law, but Huang’s Law may follow. He asserts that graphical power will escalate so that the artificial intelligence (AI)’s silicon chips will more than double in performance every 2 years. This will enhance both hardware and software to boost autonomy in ‘‘cars, trucks and ships to the face, voice and object recognition in our personal gadgets.’’ This impressive development will be critical for M&S, digital twins, and other areas related to how the defense and aerospace industry exploit AI/machine learning (ML) on future systems for aerial, sea, and ground platforms. With all the work in the simulation market development for these technologies (e.g., digital engineering), we will need proven and reliable processes (e.g., agile) led and run by skill simulation managers, engineers, and software developers for the next phase of M&S (Simulation engineers are responsible for creating virtual simulation models to test various kinds of machines and vehicles in virtual environments. The machines could be anything from submarines to fighter jets. Simulation engineers analyze these machines and other products, based on parameters like durability, performance, and safety. Based on their skill set, these professionals could work across different branches of science and manufacturing. A simulation engineer’s work is primarily to show and observe how a set of ideas and theories would unfold in the real world. Thus, the candidate should also be imaginative and creative, in addition to being technically skilled. From Leidos: Preferred M&S candidate qualifications are 1)managing a modeling, simulation and/or training organization, 2) deep understanding of DoD live, virtual and constructive training systems, 3) proven success transitioning MS&T technology programs to operational use, 4) knowledge of innovative industry business models and corresponding ‘‘go to market’’ solutions to aid in further growth. The key to success in the simulation software development is the use of agile software development. Processes like IBM’s Engineering Lifecycle Management with AI solution cover all aspects of software development from requirements to delivery with tools that maintain a pulse on what the customer needs, ensuring that those needs are met. In 2018, the Under Secretary of Defense for Acquisition, Technology, and Logistics, Honorable Ellen Lord, stated, ‘‘The current DoD process of producing and accrediting that environment is unacceptably long-months, instead of hours or days.’’ DoD needs to follow an agile like the process in Figure 1. In this manner, the Synthetic Training Environment one of their objectives is ‘‘Instead of taking 120 plus days to produce all of the components necessary to execute an exercise the necessary elements are already in the system and easily manipulated locally using state of the art software reducing cost and time.’’ According to Blair et al., model-based systems engineering (MBSE) is being utilized in several enterprise applications to manage large, complex, long life-cycle systems such as ballistic missile defense systems. In addition, M&Ss are heavily employed to support performance assessment throughout a system’s life cycle, with an emphasis on utilizing M&S to aid and reduce cost within development, integration, and test. MBSE is commonly applied early in the systems engineering process to support requirement derivation and architecture definition.