Skip to content

Collection of peer-reviewed Digital Twins developed at the University of Birmingham in partnership with industrial collaborators.

License

Notifications You must be signed in to change notification settings

uob-positron-imaging-centre/DigitalTwins

Repository files navigation

DigitalTwins

Collection of peer-reviewed Digital Twins developed at the University of Birmingham in partnership with industrial collaborators.

The code hosted in this repository has been carefully developed to allow large-scale parallel simulations in isolated, well-defined directories. They can all be generated and driven directly by Python scripts, allowing complex case studies to be constructed - e.g. sensitivity analysis, evolutionary-based parallel calibration or correlation discovery.

Acknowledgements and Funding

The authors gratefully acknowledge funding from the EPSRC Future Manufacturing Hub in Manufacture using Advanced Powder Processes, grant number 944885 and the University of Birmingham's BlueBEAR supercomputing service which was used extensively while developing the ACCES algorithm.

[TODO: list other funding & support we received]

Citing

If you use this library in your research, you are kindly asked to cite:

[TODO: add digital twins papers after publication]

License

The digital twins hosted in this repository are licensed under GPL v3.0. In non-lawyer terms, the key points of this license are:

  • You can view, use, copy and modify this code freely.
  • Your modifications must also be licensed with GPL v3.0 or later.
  • If you share your modifications with someone, you have to include the source code as well.

Essentially do whatever you want with the code, but don't try selling it saying it's yours :). This is a community-driven collection building upon many other open-source projects (GCC, LIGGGHTS, even Python itself!) without which this project simply would not have been possible. GPL v3.0 is indeed a very strong copyleft license; it was deliberately chosen to maintain the openness and transparency of great software and progress, and respect the researchers pushing granular materials forward. Frankly, open collaboration is way more efficient than closed, for-profit competition.

Copyright (C) 2021 the DigitalTwins developers. Until now, this collection was built directly or indirectly through the brain-time of:

  • Dominik Werner (University of Birmingham)
  • Andrei Leonard Nicusan (University of Birmingham)
  • Ben Jenkins (University of Birmingham)
  • Jack Sykes (University of Birmingham)
  • Dr. Kit Windows-Yule (University of Birmingham)
  • Prof. Jonathan Seville (University of Birmingham)
  • Aurelien Neveu (GranuTools)
  • Geoffroy Lumay (University of Liege, GranuTools)
  • Filip Francqui (GranuTools)
  • Laura Shaw (Freeman Technology)

Thank you.

About

Collection of peer-reviewed Digital Twins developed at the University of Birmingham in partnership with industrial collaborators.

Topics

Resources

License

Stars

Watchers

Forks

Contributors 4

  •  
  •  
  •  
  •