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Put arxiv reference in README.md
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jkneifl authored Nov 12, 2024
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Expand Up @@ -15,8 +15,8 @@ The preprint is available on [arXiv](https://doi.org/10.48550/arXiv.2408.08185).
If you use this project for academic work, please consider citing it

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Rettberg, J., Kneifl, J., Herb, J., Buchfink, P., Fehr, J., & Haasdonk, B. (2024).
Data-driven identification of latent port-Hamiltonian systems. arXiv preprint arXiv:2408.08185.
Johannes Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, Jörg Fehr, and Bernard Haasdonk.
Data-driven identification of latent port-Hamiltonian systems. Arxiv, 2024.

## Abstract
Conventional physics-based modeling techniques involve high effort, e.g.~time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes reliability. To mitigate this, we present a data-driven system identification framework that derives models in the port-Hamiltonian (pH) formulation.
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