The rise of digital twins

This issue of Nature Computational Science includes a Focus that highlights recent advancements, challenges, and opportunities in the development and use of digital twins across different domains.

Digital twins hold immense promise in accelerating scientific discovery, but the publicity currently outweighs the evidence base of success. We summarize key research opportunities in the computational sciences to enable digital twin technologies, as identified by a recent National Academies of Sciences, Engineering, and Medicine consensus study report.

Urban digital twins hold immense promise as live computational models of cities, synthesizing diverse knowledge, streaming data, and supporting decisions towards more inclusive planning and policy. The size, heterogeneity, and open-ended character of cities, however, pose many difficult questions, at the frontiers of what is currently possible in computational science. Overcoming these challenges provides pathways for fundamental progress in the field and a proving ground for its economic value and social relevance.

Digital twins of Earth have the capability to offer versatile access to detailed information on our changing world, helping societies to adapt to climate change and to manage the effects of local impacts, globally. Nevertheless, human interaction with digital twins requires advances in computational science, particularly where complex geophysical data is turned into information to support decision making.

The application of digital twins in industry has become increasingly common, but not without important challenges to be addressed by the research community.

While there is a clear opportunity for digital twins to bring value in mechanical and aerospace engineering, they must be considered as an asset in their own right so that their full potential can be realized.

The digital twin concept, while initially formulated and developed in industry and engineering, has compelling potential applications in medicine. There are, however, major challenges that need to be overcome to fully embrace digital twin technology in the medical context.

Although digital twins first originated as models of physical systems, they are rapidly being applied to social systems, such as cities. This Perspective discusses the development and use of digital twins for urban planning.

Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi.

Development in digital-twin technology has been rapidly growing across a range of industries and disciplines. However, to ensure a wider and more robust adoption of such technology, various challenges must be addressed by the computational science community.

This work proposes a probabilistic graphical model as a formal mathematical foundation for digital twins, and demonstrates how this model supports principled data assimilation, optimal control and end-to-end uncertainty quantification.

In this work, authors explore DC-DC converter monitoring and control and demonstrate a generalizable digital twin based buck converter system that enables dynamic synchronization even under reference value changes, physical system model variation, and physical controller failure.

There have been substantial developments in weather and climate prediction over the past few decades, attributable to advances in computational science. The rise of new technologies poses challenges to these developments, but also brings opportunities for new progress in the field.

For its green transition, the EU plans to fund the development of digital twins of Earth. For these twins to be more than big data atlases, they must create a qualitatively new Earth system simulation and observation capability using a methodological framework responsible for exceptional advances in numerical weather prediction.

A digital twin of Earth would fully integrate Big Data observations within an Earth–human system model, to assess the interactions between these subsystems. This Review explores the current progress in Big Data manipulation in Earth sciences providing pathways toward digital twins of Earth.

Digital twins — virtual replicas of natural systems — are emerging as promising tools for assessing seismic hazard and for aiding disaster decision-making and earthquake rapid response. However, to truly harness their potential, the challenges of exascale computing must be tackled to create systems that are capable of adapting to ever-evolving earthquake dynamics.

Many cities are vulnerable to disaster-related mortality and economic loss. Smart City Digital Twins can be used to facilitate disaster decision-making and influence policy, but first they must accurately capture, predict, and adapt to the city’s dynamics, including the varying pace at which changes unfold.

While digital twins have been recently used to represent cities and their physical structures, integrating complexity science into the digital twin approach will be key to deliver more explicable and trustworthy models and results.

As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its progress. Despite hidden variables and ‘unknown unknowns’ in datasets that may impede the realization of a digital twin for the laboratory flask, there are many opportunities to leverage AI and large datasets to advance synthesis science.

A self-driving catalysis laboratory, Fast-Cat, is presented for efficient high-throughput screening of high-pressure, high-temperature, gas–liquid reaction conditions using rhodium-catalyzed hydroformylation as a case study. Fast-Cat is used to Pareto map the reaction space and investigate the varying performance of several phosphorus-based hydroformylation ligands.

Precision medicine envisages a changed paradigm for health care through better understanding of individual disease susceptibility and prognosis, enabling more personalized treatment. Enabling technologies such as the health digital twin are rapidly evolving, presenting important challenges and opportunities to be tackled within local contexts.

The rapid development of on-body sensors and multimodal artificial intelligence is accelerating the emergence of the human body digital twin (DT) as an interdisciplinary research topic. This Perspective outlines a roadmap for applications of human body DTs in health monitoring, disease prevention and treatment.

Theodorescu and colleagues describe a Molecular Twin approach that integrates multi-omic and computational pathology data from patients with pancreatic ductal adenocarcinoma using artificial intelligence to predict clinical outcomes.

Little is known about the potential of digital twins in the pursuit of sustainability. This study examines the likely benefits of digital twins in urban sustainability paradigms, their limitations when modelling socio-technical and socio-ecological systems and possible ways to attenuate them.

From COVID-19 to the war in Ukraine, virtual models could inform global food policy before emergencies unfold.

A physics-based digital twin simulating the physical, physiological and microbiological behaviour of citrus fruits shipped at sub-zero temperatures reveals that half of the shipments lie outside the ideal trade-off range between maintaining quality, killing fruit flies and avoiding chilling injury.

A digital twin represents a real world object using available data. Here, the authors develop a digital twin for mandaring orchards in Jeju island showing the value of individualized agriculture to predict fruit quality at tree level.

Digital twins can be used to support planetary operations and analysis. Here, the authors show tri-aspect characterization of lunar far side regolith and investigate the origin of a fin-shaped rock via digital twin of Yutu-2 rover.