pyMOR

We are proud to announce the release of pyMOR 2025.2! This release includes improved support for data-driven model order reduction using machine learning methods such as artificial neural networks or the Vectorial Kernel Orthogonal Greedy Algorithm (VKOGA). Support for saddle-point problems, such as the Stokes equations, has been improved, including dedicated supremizer enrichment and least-squares Petrov-Galerkin (LSPG) reductors. Furthermore, the Q-DEIM algorithm for empirical interpolation and bindings for the FEniCSx PDE solver library have been added. pyMOR 2025.2 contains contributions by Maximilian Bindhak, Timo Plath, Sven Ullmann and @franciuprise. See here for more details.