The field of model order reduction and structure-preserving discretization is experiencing significant advancements, driven by the development of innovative methods and techniques. A key direction in this field is the creation of efficient and robust algorithms for reducing the dimensionality of complex systems, while preserving their physical properties and structure. This is being achieved through the development of new discretization methods, such as structure-preserving finite element methods, and the improvement of existing model order reduction techniques, like shifted Cholesky QR. Additionally, the application of these methods to various fields, including fluid dynamics and physics-based simulations, is showcasing their potential for drastic cost savings and improved accuracy. Noteworthy papers in this regard include: An overlapping domain decomposition method for parametric Stokes and Stokes-Darcy problems, which presents a non-intrusive framework for reducing the dimensionality of parametric flow fields. Structure-preserving space discretization of differential and nonlocal constitutive relations for port-Hamiltonian systems, which introduces a structure-preserving finite element method for preserving the physical properties of complex systems. Towards an Efficient Shifted Cholesky QR for Applications in Model Order Reduction, which proposes an improved shifting strategy for highly ill-conditioned matrices, leading to vastly superior performance on memory-bandwidth-limited problems. Meshless projection model-order reduction via reference spaces for smoothed-particle hydrodynamics, which introduces a model-order reduction framework for meshless weakly compressible smoothed particle hydrodynamics methods.