%0 Journal Article %1 Martinuzzi2022-ax %A Martinuzzi, Francesco %A Rackauckas, Chris %A Abdelrehim, Anas %A Mahecha, Miguel D %A Mora, Karin %D 2022 %I arXiv %K %T ReservoirComputing.Jl: An efficient and modular library for reservoir computing models %X We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. The software offers a great number of algorithms presented in the literature, and allows to expand on them with both internal and external tools in a simple way. The implementation is highly modular, fast and comes with a comprehensive documentation, which includes reproduced experiments from literature. The code and documentation are hosted on Github under an MIT license https://github.com/SciML/ReservoirComputing.jl.

@article{Martinuzzi2022-ax, abstract = {We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. The software offers a great number of algorithms presented in the literature, and allows to expand on them with both internal and external tools in a simple way. The implementation is highly modular, fast and comes with a comprehensive documentation, which includes reproduced experiments from literature. The code and documentation are hosted on Github under an MIT license https://github.com/SciML/ReservoirComputing.jl.}, added-at = {2024-09-10T11:56:37.000+0200}, author = {Martinuzzi, Francesco and Rackauckas, Chris and Abdelrehim, Anas and Mahecha, Miguel D and Mora, Karin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2c1e9c33e35bcb6ab6ae235a359778cc6/scadsfct}, interhash = {6515f92bbc21da90bf3526a29409abae}, intrahash = {c1e9c33e35bcb6ab6ae235a359778cc6}, keywords = {}, publisher = {arXiv}, timestamp = {2024-09-10T15:15:57.000+0200}, title = {{ReservoirComputing.Jl}: An efficient and modular library for reservoir computing models}, year = 2022 }