As engineering applications face escalating demands for sustainability, efficiency, and resilience, there is a pressing need for advanced digital frameworks that support design innovation, continuous monitoring, and informed decision-making. Digital Twins provide a transformative solution: serving as a continuously evolving virtual counterpart of physical systems that integrates physics-based models, multisource data, and uncertainty quantification to enable real-time insight, predictive analytics, and lifecycle optimization for next-generation greener technologies [1-3]. This minisymposium will showcase recent advances in digital twin methodologies for sustainable engineering applications across air, land, and sea. Topics of interest include the integration of scientific machine learning, surrogate modeling, and model reduction techniques for real-time performance, as well as Bayesian approaches to inverse problems. Additional themes will cover data assimilation and continuous model updating, interpretable machine learning, multisource and multifidelity active learning, along with uncertainty quantification and propagation. Contributions demonstrating applications to predictive maintenance, energy efficiency, emission reduction, and lifecycle design optimization are particularly encouraged. The minisymposium will highlight both methodological innovations and cross-domain applications, illustrating the power and versatility of the digital twin paradigm in advancing sustainable engineering solutions. REFERENCES [1] AIAA Digital Engineering Outreach and Integration Committee, Digital twin: Definition & value, AIAA and AIA Position Paper, 2020. [2] L. Mainini, M. Diez, Digital Twins and their Mathematical Souls, In STO-MP-AVT369 Research Symposium on Digital Twin Technology Development and Application for Tri-Service Platforms and Systems, 2023. [3] The National Academies Collection: Reports funded by National Institutes of Health, Foundational Research Gaps and Future Directions for Digital Twins, Washington DC, National Academies Press (US), 2024 Mar 28. PMID: 39088664.