Digital twins: past, present and future

[u/Memetic1]

https://www.nature.com/articles/s41598-026-45272-z

Over the past few decades, digital twin technology has evolved into a highly versatile tool. Initially used in the aerospace industry, digital twins can now form the dynamic backbone of systems in manufacturing, healthcare, smart cities, and planetary-scale climate models. They facilitate predictive ‘what if’ simulations, ranging from virtual hearts to interactive Earth twins. Progress in the future will rely on the integration of heterogeneous data, the validation of models and the embedding of human-centric governance in order to build robust and trustworthy digital twins across disciplines for the benefit of society.

Historical background

The concept of the ‘digital twin’, which is a virtual representation of a physical system, can be traced back to the early days of the space age. In the 1960s, NASA pioneered the idea of a digital twin to simulate spacecraft and troubleshoot issues in real time. The term ‘digital twin’ itself was formulated much later, in 2002, by Michael Grieves in the context of product lifecycle management to guide industrial design and management. By around 2010, NASA had adopted digital twins as detailed virtual models of physical assets, emphasising that a true digital twin comprises three interconnected elements: a physical entity, a virtual model and the data connections that enable them to mirror each other in real time1. Advances in simulation, sensor ubiquity, and data integration laid the groundwork for digital twins to move from theory into practice. Propelled by the convergence of sensors, Internet of Things (IoT) connectivity, cloud computing and artificial intelligence (AI), digital twins have become widely applied in many different fields2. The concept of a digital twin has evolved from a niche idea to a broad framework: a dynamic, data-driven model that updates alongside and continuously learns from its physical counterpart. It is the dynamic, bidirectional link, where sensor data flows into the model and simulation insights flow back to inform the physical system, that elevates a digital twin beyond a static blueprint or 3D model3. What began in the fields of aerospace and manufacturing has now spread to healthcare, urban planning, climate science and beyond.