Digital twins are virtual replicas of physical objects, systems, processes, or services. Intelligent digital twins (IDT) are integrated with artificial intelligence (AI), machine learning (ML), and other advanced technologies. The dynamic, predictive models can learn and adapt over time.
Michael Grieves, executive director of the Digital Twin Institute, broke down the key attributes of IDTs in a recent paper proposing a framework for better managing complex systems and emergent behaviors.
IDTs actively interact with their physical counterparts and data ecosystems. They ingest data on their twins, continuously updating themselves to reflect real-time conditions and changes.
IDTs operate in an always connected environment, leveraging cloud computing, IoT devices, and other digital infrastructures to access, process, and predict outcomes in real-time.
IDTs are engineered with specific outcomes in mind. They use AI to identify and execute actions that align with desired outcomes like optimizing performance and reducing downtime.
IDTs conduct ongoing simulations to forecast future conditions, identify potential disruptions, and preemptively address issues to ensure assigned tasks are met.
Interest in digital twinning has grown alongside AI and ML. The evolution toward IDTs is a significant shift toward autonomous, predictive, and prescriptive digital tools.