SIMNET (Synthetic Environment for Military Training) refers to a virtual reality simulator developed in the 1980s that allowed a large number of military personal to train together in a simulated battlefield environment. While SIMNET showed promise for improving realistic large-scale training, transitioning this technology for comprehensive training programs faced significant challenges.

One of the biggest hurdles was the lack of available computing power needed to run sophisticated simulations for hundreds or thousands of virtual entities simultaneously interacting in real-time. The early SIMNET prototypes in the 1980s were only able to simulate a small number of entities at once due to the limitations of processors, memory, and graphics capabilities available at that time. Scaling the simulations up to unit, battalion, or even higher brigade level training would have overwhelmed all but the most advanced supercomputers. Additional computing resources would have been required at each training location to distribute the processing load. The high costs associated with procuring and maintaining sufficient hardware posed budgetary challenges for wide deployment.

Network connectivity and bandwidth also presented major issues. SIMNET’s distributed architecture relied on linking processor nodes across local area networks, but the underlying network infrastructures of the 1980s and 90s were not equipped to support high-bandwidth communications across nodes separated by long distances. Transmitting continuous simulation data, entity states, 3D graphical scenes, and communications between hundreds of mobile platforms engaged in long-range virtual maneuvers would have saturated most available networks. Inconsistent network performance could also jeopardize the real-time nature of simulations. Additional networking equipment, higher capacity links, and new communication protocols may have been needed.

Software development forscaledSIMNET simulations posedtechnicalhurdlesaswell.ThecoreSIMNET software system was designed assuming smaller numbers of interactive entities and a focus on individual platform dynamics. Extending the behavior, sensor, weaponry, and interaction modeling to thousands of land, air, and sea platforms across wide virtual battlespaces within centralized control and data management would have required rearchitecting and re-engineering large portions of the underlying simulation software. Distributed software architectures, artificial intelligence, automated entity management, scenario generation tools, and enhanced 3D rendering engines may have needed development.

Interoperability betweenSIMNET nodesfrom different servicebranches andcoalition partnerswould have been problematic without common simulation standards and protocols. Each organization employed diverse simulation systems with unique data formats, interfaces, and functionality. Integrating heterogeneous simulators across units and multinational partners to train together could have been immensely challenging without consensus on technical specifications, messaging schemes, and data representation. Lengthy standardization efforts may have been required to develop comprehensive interoperability specifications.

Another consideration is that large-scale virtual training scenarios may have impacted realism if not carefully designed. Unconstrained interactions between hundreds or thousands of semi-autonomous virtual entities risks creating unrealistic “canned” scenarios and losing the element of emergent behaviors that stem from chaos and unpredictability on the battlefield. Scenario generation tools and artificial intelligence models would need to be highly sophisticated to maintain realism and unpredictability as numbers increase while still meeting training objectives.

While SIMNET showed the potential for virtual collective training, full implementation of large-scale SIMNET simulations faced substantial hurdles in available computing power, networking capability, software complexity, interoperability standardization, and scenario design that likely exceeded the technologies of the 1980s and 1990s. Overcoming these challenges would have required massive investments and long development timelines. Later advances like faster processors, networked computing clusters, broadband networks, modular simulation architectures, and artificial intelligence have helped modern virtual environments gradually overcome some of these issues, but scaling simulation realism remains an ongoing challenge.