1.0.0

Release [1.0.0] - 2026-04-07

The "Solver-Agnostic" Release

This version marks a major milestone: the transition of pyforce from a FEniCS-centric implementation to a universal, matrix-based framework. By adopting pyvista as the core backend, the library is now fully compatible with data-driven workflows from any VTK-supported solver (OpenFOAM, Ansys, etc.).

🚀 Key Features

• PyVista Integration: Native mesh handling, spatial interpolations, and 3D visualization.
• Matrix-Native Core: POD, EIM, GEIM, and PBDW now operate on standard numpy/scipy arrays.
• Universal Input: Seamlessly process snapshots from external solvers without FEM function space requirements.
• Streamlined Setup: Dropped dolfinx and mpi4py as core dependencies for faster installation and better cross-platform support.

📚 Documentation & Support

• Rewritten Docs: Complete documentation overhaul reflecting the new architecture.
• New Tutorials: Added Jupyter notebooks for non-FEM workflows and fluid dynamics.
• Migration Guide: A dedicated guide to help users transition from the FEniCS-based 0.1.x versions.

What's Changed

• Brand new version using pyvista as backend by @Steriva in #27
• Enhance testing workflow and documentation updates by @Steriva in #28

Full Changelog: 0.1.3...1.0.0

0.1.3

What's Changed

• Add joss-paper and related Github Action by @Steriva in #5
• Update License by @Steriva in #7
• Minor Update before JOSS revision by @Steriva in #11
• 10 joss submission review reviewer 2 software paper by @Steriva in #12
• Update joss paper by @Steriva in #13
• 9 joss submission review reviewer 2 functionality by @Steriva in #14
• 8 docs review by @Steriva in #15
• Update docs - review by @Steriva in #16
• 8 docs review by @Steriva in #17
• Add tests for most important classes in pyforce - Minor Fixes by @Steriva in #19
• Update Paper and Installation notes by @Steriva in #21
• Add EIM algorithm minor fixes by @Steriva in #22
• New features, new tutorial and minor fix by @Steriva in #23
• Code and paper update following comments from reviewer 2 by @Steriva in #26

Full Changelog: 0.1.2...0.1.3

pyforce 0.1.2

• Merge of the FunctionsList class with FunctionsMatrix to handle everything
• Add automatic testing and notebook testing
• Fixing tutorials

0.1.1

pyforce is a Python package implementing some Data-Driven Reduced Order Modelling (DDROM) techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. These techniques have been implemented upon the dolfinx package (currently v0.6.0), part of the FEniCSx project, to handle mesh generation, integral calculation and functions storage. The package is part of the ROSE (Reduced Order modelling with data-driven techniques for multi-phySics problEms): mathematical algorithms aimed at reducing the complexity of multi-physics models (for nuclear reactors applications), at searching for optimal sensor positions and at integrating real measures to improve the knowledge on the physical systems.

The following techniques have been implemented:

• Proper Orthogonal Decomposition with Projection and Interpolation for the Online Phase
• Generalised Empirical Interpolation Method, either regularised with Tikhonov or not
• Parameterised-Background Data-Weak formulation
• an Indirect Reconstruction algorithm to reconstruct non-observable fields

Minor fixes has been performed, plus the extension of PBDW and SGREEDY to H1 representation.

This package is aimed to be a valuable tool for other researchers, engineers, and data scientists working in various fields, not only restricted in the Nuclear Engineering world.

What's Changed

• Update main by @Steriva in #1

New Contributors

• @Steriva made their first contribution in #1

Full Changelog: 0.1.0...0.1.1

pyforce 0.1.0

pyforce is a Python package implementing some Data-Driven Reduced Order Modelling (DDROM) techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. These techniques have been implemented upon the dolfinx package (currently v0.6.0), part of the FEniCSx project, to handle mesh generation, integral calculation and functions storage. The package is part of the ROSE (Reduced Order modelling with data-driven techniques for multi-phySics problEms): mathematical algorithms aimed at reducing the complexity of multi-physics models (for nuclear reactors applications), at searching for optimal sensor positions and at integrating real measures to improve the knowledge on the physical systems.

The following techniques have been implemented:

• Proper Orthogonal Decomposition with Projection and Interpolation for the Online Phase
• Generalised Empirical Interpolation Method, either regularised with Tikhonov or not
• Parameterised-Background Data-Weak formulation
• an Indirect Reconstruction algorithm to reconstruct non-observable fields

This package is aimed to be a valuable tool for other researchers, engineers, and data scientists working in various fields, not only restricted in the Nuclear Engineering world.