Digital twins have emerged as transformative tools, enabling real-time simulation, analysis, and control of complex physical systems by integrating computational models, sensor data, and advanced analytics. This mini-symposium will highlight recent advances and emerging computational technologies driving the development, refinement, and application of digital twins across engineering and applied sciences. Topics covered include machine learning, high-fidelity modeling, data assimilation, uncertainty quantification, real-time analytics, and scalable computational platforms. The symposium seeks to foster discussions on innovative methodologies and computational frameworks, aiming to enhance the predictive capabilities, accuracy, and reliability of digital twins. By bringing together leading researchers and practitioners, the event will promote interdisciplinary collaboration, accelerating the adoption and integration of digital twins for improved decision-making and operational efficiency in engineering and applied scientific domains.