Digital Twins (DT), highly accurate virtual representations of physical entities, have revolutionized industries by integrating numerical simulations and observational data to create realistic, dynamic models. Initially developed for industrial applications, digital twins have expanded into diverse domains. They enable accurate predictions by simulating real-world performance, identifying potential issues, and iterating feedback loops for optimized decision-making. This paradigm leverages advanced computational techniques to enhance our understanding and management of complex systems. The Horizon Europe interTwin project has developed a highly generic Digital Twin Engine (DTE) to support interdisciplinary Digital Twins(DT). The project brought together infrastructure providers, technology providers and DT use cases from different domains. This group of experts enables the co-design of both the DTE Blueprint Architecture and the prototype platform; not only benefiting end users like scientists and policymakers but also DT developers. The main contributions of the DTE are: (1) a federated architecture that allows seamless integration of distributed computing and storage resources across various institutions, (2) standardized interfaces and protocols that support interoperability among different scientific fields, (3) a co-design approach that includes requirements from high-energy physics, radio astronomy, gravitational-wave astrophysics, climate research, and environmental monitoring, and (4) strong methods for assessing AI model quality, provenance, and uncertainty measurement in federated settings.
The talk will focus on the DTE software components developed and integrated, detailing some of the pilot use cases that have successfully driven the DTE implementation.