The MiniSymposium aims to bring together multidisciplinary expertise in advanced numerical techniques for managing big data in computational mechanics, including machine learning algorithms and low-rank strategies. Key areas of focus include: 1. Low-Rank Approximations in numerical solutions, such as SVD and POD for finite element solvers. 2. Tensor-Based Methods for efficient storage and processing of multi-dimensional data, including tensor decomposition and QTT methods. 3. Reduced-Order Modeling (ROM) using projection-based and data-driven ROMs in structural dynamics and fluid mechanics. 4. Machine Learning applications in computational mechanics, including supervised/unsupervised learning, PINNs, and surrogate modeling. 5. Big Data and HPC solutions, with a focus on parallel computing, memoryefficient algorithms, and distributed data storage. 6. Data-Driven and Physics-Based Models integrating multi-fidelity and multiscale modeling approaches with adaptive strategies. 7. Uncertainty Quantification and Optimization using probabilistic methods and stochastic reduced-order models. 8. Real-Time Simulation and Control involving low-rank methods in FEA for structural health monitoring and control of smart infrastructure. 9. Data Compression and Efficient Storage techniques to reduce I/O bottlenecks in HPC and manage simulation data efficiently. The MS welcomes contributions addressing applications in multi-physics and multi-scale problems, including coupled systems, materials science, geomechanics, and biomechanics, while also embracing a wide range of computational science and engineering applications.