<ul><li>Digital twin technologies help practitioners simulate, monitor, and predict undesirable outcomes in-silico, while avoiding the cost and risks of conducting live simulation exercises.</li><li>Virtual reality (VR) based digital twin technologies are especially useful when monitoring human Patterns of Life (POL) in secure nuclear facilities, where live simulation exercises are too dangerous and costly to ever perform.</li><li>The challenge of collecting data in high-security facilities led to the use of an agent-based model driven by human activity patterns to generate synthetic movement trajectories in a digital twin system called MetaPOL.</li><li>The study evaluates the efficacy of using deep neural networks to predict the simulated trajectories and distinguish NPC (non-player character) movement during normal operations from that during a simulated emergency response scenario.</li></ul>

Agent-Based Modeling and Deep Neural Networks for Establishing Digital Twins of Secure Facilities under Sensing Restrictions

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