Digital Twins

Digital twins are computational counterparts of physical assets, people, or broader systems, updated with observations to represent their changing state and test possible actions. Approaches range from physics-based simulation and data assimilation to machine-learning surrogates, hybrid models, generative agents, and reinforcement learning for control. Applications span manufacturing, infrastructure, transportation, healthcare, energy, and social systems, where twins can support forecasting, maintenance, safety testing, and personalized decisions. A central challenge is ensuring that a twin remains calibrated and trustworthy as conditions change, while balancing fidelity, uncertainty, computation, privacy, and validation against real-world outcomes.