The solution of large and sparse linear systems represents a major bottleneck in many areas of computational science and engineering, such as computational fluid dynamics, magnetohydrodynamics, and structural mechanics. In this sense, real-world applications demanding a high resolution usually lead to seriously ill-conditioned, extreme-scale problems, triggering a complex interplay between numerical reliability and computational efficiency. While sparse direct solvers offer strong numerical robustness, their high memory requirements and operational complexity, and reduced parallelism limit their applicability [1]. In contrast, iterative Krylov subspace methods offer an easier implementation and parallelisation, but their convergence is very sensitive to the coefficient matrix's spectrum and always require powerful preconditioners whose choice, design, and implementation remain among the most active research fields in numerical linear algebra [2]. This minisymposium welcomes contributions on sparse linear solvers for large-scale simulations of real-world applications. Topics of interest include, but are not limited to: - New developments in general-purpose and physics-based preconditioners - New algorithms tailored for modern HPC systems, such as matrix-free, communication- avoiding, mixed-precision, and GPU-friendly algorithmic redesign - Data-driven strategies for preconditioning and solver parameter tuning - Benchmarking, performance modelling, and reproducibility in solver development - Integration of solvers and preconditioners into simulation frameworks The goal of this minisymposium is to promote exchange between linear solver developers and application experts, fostering discussion across disciplines and applications. REFERENCES: [1] T. A. Davis, S. Rajamanickam, and W. M. Sid-Lakhdar (2016). A survey of direct methods for sparse linear systems. Acta Numerica, 25, 383–566. [2] M. Benzi (2002). Preconditioning Techniques for Large Linear Systems: A Survey. Journal of Computational Physics, 182(2), 418–477.