This article from *Nature Computational Science* highlights the advancements, challenges, and opportunities in the development and application of digital twins across various domains. It begins by recalling NASA's use of high-fidelity simulators during the Apollo 13 mission, which predated the term "digital twin" by over 40 years. Since then, significant technological advancements have been made, including the generation of vast amounts of real-time data, sophisticated modeling capabilities, and the integration of machine learning. The article discusses how digital twins have been leveraged in engineering and industrial applications, such as aerospace and manufacturing, and their recent expansion into biomedical, climate, and social sciences. Despite the growing potential, the authors emphasize the numerous challenges, including the trade-offs between model complexity and accuracy, the need for validation benchmarks and international standards, and the importance of human-in-the-loop interactions. The perspectives and comments in the issue address specific domains, such as aerospace and mechanical engineering, biomedical sciences, urban planning, and Earth systems science. They highlight the unique challenges and opportunities in each field, such as the need for flexible human interaction in urban planning and the importance of protecting privacy in biomedical applications. The National Academies of Sciences, Engineering, and Medicine (NASEM) report identifies gaps in the research underlying digital twin technology and proposes a cross-domain definition, emphasizing the critical role of verification, validation, and uncertainty quantification. The article concludes by emphasizing the need for an integrated research agenda and interdisciplinary collaboration to fully realize the potential of digital twins.This article from *Nature Computational Science* highlights the advancements, challenges, and opportunities in the development and application of digital twins across various domains. It begins by recalling NASA's use of high-fidelity simulators during the Apollo 13 mission, which predated the term "digital twin" by over 40 years. Since then, significant technological advancements have been made, including the generation of vast amounts of real-time data, sophisticated modeling capabilities, and the integration of machine learning. The article discusses how digital twins have been leveraged in engineering and industrial applications, such as aerospace and manufacturing, and their recent expansion into biomedical, climate, and social sciences. Despite the growing potential, the authors emphasize the numerous challenges, including the trade-offs between model complexity and accuracy, the need for validation benchmarks and international standards, and the importance of human-in-the-loop interactions. The perspectives and comments in the issue address specific domains, such as aerospace and mechanical engineering, biomedical sciences, urban planning, and Earth systems science. They highlight the unique challenges and opportunities in each field, such as the need for flexible human interaction in urban planning and the importance of protecting privacy in biomedical applications. The National Academies of Sciences, Engineering, and Medicine (NASEM) report identifies gaps in the research underlying digital twin technology and proposes a cross-domain definition, emphasizing the critical role of verification, validation, and uncertainty quantification. The article concludes by emphasizing the need for an integrated research agenda and interdisciplinary collaboration to fully realize the potential of digital twins.