https://github.com/adrianlepp/nonlinear-lodegp-control
Science Score: 36.0%
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○Scientific vocabulary similarity
Low similarity (10.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: adrianLepp
- License: mit
- Language: Python
- Default Branch: main
- Size: 86.9 KB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
nonlinearlodegpcontrol
This project is based on the LODE-GP and was developed in my master thesis Physics-informed Gaussian Process Regression: Applications in Modelling and Control of Nonlinear Systems.
For the part about Model Predictive Control (MPC), check the pulbication Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems. Everything else is not publicated yet.
Check the examples, on how to use the implemented methods.
Requirements
For installation run
bash
git clone git@github.com:adrianLepp/nonlinear-lodegp-control.git
pip install -e ./nonlinear-lodegp-control
SageMath (https://www.sagemath.org/) needs to be installed seperately:
```bash
Example install via conda (recommended):
conda install -c conda-forge ```
Remark on gpytorch
The heteroskedastik noise for a multi-dimensional GP (as it is used for MPC), is not implemented in gpytorch yet. See https://github.com/cornellius-gp/gpytorch/issues/901 for proposed implementations. One possible implementation is present in this repository.
Calling the likelihood on another distribution than the training data is also not possible, due to a bug that is not fixed yet. See https://github.com/cornellius-gp/gpytorch/issues/2630 for a description of this bug and a patch that can be applied to the source code. However, it is normally not necessary to call the likelihood on something else than the training data.
Owner
- Login: adrianLepp
- Kind: user
- Repositories: 1
- Profile: https://github.com/adrianLepp
GitHub Events
Total
- Push event: 4
- Create event: 4
Last Year
- Push event: 4
- Create event: 4
Dependencies
- einops *
- gpytorch *
- matplotlib *
- numpy *
- sage *
- scipy *