Python package for design of experiments.

Currently implements:

• State uncertainty
• Control input uncertainty
• Delta constraints on control input
• Mean (latent and observed) state constraints
• Design criteria
• Discrimination criteria
• SLSQP optimisation
• Model parameter uncertainty

To do:

• Separate time points for controls, measurements and constraints
• optimisation of initial state
• prepare for optimisation of measurement time points
• gradients (continuous time)
• one-model option (prepare for nMPC and DOP?)
• documentation
• tests
• merge branches
• Static models
• Function in ProblemInstance to specify what constraint(s) is violated

Pronounciation

We pronounce the package name as 'dopey'

Authors

• Simon Olofsson (scwolof) - Imperial College London
• Eduardo Schultz (eduschultz) - RWTH Aachen

License

The GPdode package is released under the MIT License. Please refer to the LICENSE file for details.

Acknowledgements

This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no.675251, and from Imperial College London's Data Science Institute Seed Fund.