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.