The development of reliable and real-time digital twins for engineering structures remains a major challenge due to the high computational cost of conventional numerical simulations, limited sensor data availability, model uncertainties, and difficulties in integrating physics-based models with data-driven approaches. Recent advances in scientific machine learning, particularly Physics-Informed Neural Networks (PINNs) and related physics-guided learning frameworks, have opened new opportunities for constructing robust and efficient digital twins in computational solid mechanics. This minisymposium aims to bring together researchers working on physics-informed and hybrid machine learning methodologies for digital twin applications in solid mechanics. The session will focus on emerging computational frameworks that integrate governing physical laws, experimental observations, sensor data, and numerical simulations within unified predictive environments. Particular emphasis will be placed on PINNs, neural operators, reduced-order modeling, operator-learning techniques, and hybrid finite-element–machine-learning approaches for forward and inverse problems. Topics of interest include, but are not limited to, digital twins for structural systems, constitutive parameter identification, structural health monitoring, uncertainty quantification, multiscale and multiphysics modeling, real-time simulation, surrogate modeling, data assimilation, and AI-enhanced computational mechanics. Contributions addressing scalability, robustness, computational efficiency, industrial deployment, and real-world engineering applications of PINN-based digital twin frameworks are especially encouraged. The minisymposium intends to foster interdisciplinary discussions among researchers in computational mechanics, scientific machine learning, applied mathematics, and engineering sciences, while highlighting current challenges and future research directions for next-generation physics-informed digital twins in solid mechanics.