This repository contains the python code that was presented for the IFAC.
Adachi, M., Kuhn, Y., Horstmann, B., Osborne, M. A., Howey, D. A. Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature, IFAC 2023 link
This work is based on the BASQ repository
Recently we have published a new method that achieves faster convergence.
https://github.com/ma921/SOBER
Try it out the tutorial 05 for comparing.
- fast Bayesian inference via Bayesian quadrature
- Simultaneous inference of Bayesian model evidence and posterior
- GPU acceleration
- Canonical equivalent circuit model (ECM)
- Statistical analysis computation of the ECM
- PyTorch
- GPyTorch
- BoTorch
- functorch
Open "ECM_model_selection.ipynb". This will give you a step-by-step introduction.
Please cite this work as
@article{adachi2023bayesian,
title={Bayesian model selection of lithium-ion battery models via {B}ayesian quadrature},
author={Adachi, Masaki and Kuhn, Yannick and Horstmann, Birger and Latz, Arnulf and Osborne, Michael A and Howey, David A},
journal={IFAC-PapersOnLine},
volume={56},
number={2},
pages={10521--10526},
year={2023},
doi={https://doi.org/10.1016/j.ifacol.2023.10.1073},
publisher={Elsevier}
}
Also please consider to cite this work as well.
@article{adachi2022fast,
title={Fast {B}ayesian inference with batch {B}ayesian quadrature via kernel recombination},
author={Adachi, Masaki and Hayakawa, Satoshi and J{\o}rgensen, Martin and Oberhauser, Harald and Osborne, Michael A},
journal={Advances in Neural Information Processing Systems},
volume={35},
doi={https://doi.org/10.48550/arXiv.2206.04734},
year={2022}
}