pyforce: Python Framework for data-driven model Order Reduction of multi-physiCs problEms - 0.1.3

Code implementation after review process from JOSS.

What's Changed

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

Full Changelog: https://github.com/ERMETE-Lab/ROSE-pyforce/compare/0.1.2...0.1.3

- Jupyter Notebook
Published by Steriva 6 months ago

pyforce: Python Framework for data-driven model Order Reduction of multi-physiCs problEms - pyforce 0.1.2

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

- Jupyter Notebook
Published by Steriva almost 2 years ago

pyforce: Python Framework for data-driven model Order Reduction of multi-physiCs problEms - 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 https://github.com/ERMETE-Lab/ROSE-pyforce/pull/1

New Contributors

• @Steriva made their first contribution in https://github.com/ERMETE-Lab/ROSE-pyforce/pull/1

Full Changelog: https://github.com/ERMETE-Lab/ROSE-pyforce/compare/0.1.0...0.1.1

- Jupyter Notebook
Published by Steriva about 2 years ago

pyforce: Python Framework for data-driven model Order Reduction of multi-physiCs problEms - 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.

- Jupyter Notebook
Published by Steriva about 2 years ago