Digital twin is an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools have brought the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Digital twin can be referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype. It plays a transformative role in how we design and operate cyber-physical intelligent systems and how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by current, evolutionary modeling practices. This paper reviews the recent status of methodologies and techniques related to the construction of digital twins, providing a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders. The paper discusses the values of digital twin, including real-time remote monitoring and control, greater efficiency and safety, predictive maintenance and scheduling, scenario and risk assessment, better intra- and inter-team synergy and collaboration, more efficient and informed decision support system, personalization of products and services, and better documentation and communication. It also explores diverse applications of digital twin in health, meteorology, manufacturing and process technology, education, and cities, transportation and energy sectors. Common challenges in building digital twins include data security and management, data quality improvements, latency, real-time simulations, large scale data fusion and assimilation, intelligent data analytics, predictive capacity, transparency and generalization of technologies across diverse application areas. Enabling technologies include physics-based modeling, data-driven modeling, big data cybernetics, infrastructure and platforms, and human-machine interface. Physics-based modeling involves experimental modeling, 3D modeling, and high fidelity numerical simulators. Data-driven modeling includes data generation, preprocessing, management and ownership, data privacy and ethical issues, and machine learning and artificial intelligence. Big data cybernetics involves the art of steering a system towards a reference point using data. The paper concludes with reflections and recommendations targeted towards five distinct stakeholders.Digital twin is an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools have brought the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Digital twin can be referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype. It plays a transformative role in how we design and operate cyber-physical intelligent systems and how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by current, evolutionary modeling practices. This paper reviews the recent status of methodologies and techniques related to the construction of digital twins, providing a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders. The paper discusses the values of digital twin, including real-time remote monitoring and control, greater efficiency and safety, predictive maintenance and scheduling, scenario and risk assessment, better intra- and inter-team synergy and collaboration, more efficient and informed decision support system, personalization of products and services, and better documentation and communication. It also explores diverse applications of digital twin in health, meteorology, manufacturing and process technology, education, and cities, transportation and energy sectors. Common challenges in building digital twins include data security and management, data quality improvements, latency, real-time simulations, large scale data fusion and assimilation, intelligent data analytics, predictive capacity, transparency and generalization of technologies across diverse application areas. Enabling technologies include physics-based modeling, data-driven modeling, big data cybernetics, infrastructure and platforms, and human-machine interface. Physics-based modeling involves experimental modeling, 3D modeling, and high fidelity numerical simulators. Data-driven modeling includes data generation, preprocessing, management and ownership, data privacy and ethical issues, and machine learning and artificial intelligence. Big data cybernetics involves the art of steering a system towards a reference point using data. The paper concludes with reflections and recommendations targeted towards five distinct stakeholders.