Digital twins are transforming how scientists study and improve complex systems - reducing the time between discovery and delivery.
Learn what a digital twin is and how researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) are combining real-time data, physics-based models, and artificial intelligence to create dynamic, living simulations. By integrating AI with continuous streams of sensor and historical data, these digital twins can predict performance, uncover hidden patterns, test changes virtually, and optimize systems faster than traditional experimentation alone.
The result: smarter decisions, fewer costly trials, and faster scientific breakthroughs across disciplines.
A digital twin is a virtual model of a real-world tool or system. But it's more than a static blueprint or a carbon copy - it's a living, physics-based model that stays in sync with its physical counterpart. That means researchers can explore potential changes to the system before they make them, and project the outcomes using real data.
You can think of it like the difference between a paper map and a virtual map with GPS. A paper map is a snapshot in time, but it doesn't update and respond to real-time changes. A virtual map with GPS shows your location, but also combines past traffic patterns, current road conditions, and real-time reports to predict your arrival time and adjust on the fly. Similarly, digital twins take in constant streams of sensor information and historic data from the real system - allowing it to accurately predict behavior and offer alternative routes.
This ability to simulate changes, whether in real-time or retrospectively, allows researchers to explore possibilities without risking time, resources, or damage to the actual system.
Berkeley Lab researchers are working on digital twins across science disciplines. These include lasers and accelerators that need precise alignment, buildings that need to balance energy use with comfort, and even bioreactors working to increase biofuel production without losing delicate cells.
The goal is the same, no matter the field: make better decisions, faster, with greater understanding, and far less trial and error.
To learn more about the research behind this technology, visit lbl.gov.
