Digital Twins for Complex Systems: Theory and Algorithms

Digital twins represent the next frontier in the impact of computational science on grand challenges across science, technology, and society. A digital twin is a computational model or set of coupled models that evolves over time to persistently represent the structure, behavior, and context of a unique physical system, process, or biological entity. Bidirectional interaction between the physical system and its virtual counterpart is central to the digital twin concept. While there remains multiple different definitions and interpretations of a digital twin, the 2024 National Academies study has helped to consolidate and align many aspects of the academic community, especially around the key roles of data assimilation, optimal control, and uncertainty quantification. This working group will bring together researchers who are at the forefront of foundational advances in these areas of digital twin theories and algorithms, and their application to complex systems.